Assessing Landslide Susceptibility of Türkiye at Drainage Basin Scale: A Semi-quantitative Approach

Assessing Landslide Susceptibility of Türkiye at Drainage Basin Scale: A Semi-quantitative Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing Landslide Susceptibility of Türkiye at Drainage Basin Scale: A Semi-quantitative Approach Kıvanç Okalp, Haluk Akgün This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4704929/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study uses the Analytical Hierarchy Process (AHP) integrated with Geographic Information Systems (GIS) to assess landslide susceptibility in Türkiye at a drainage basin scale. Türkiye's mountainous terrain makes it highly prone to landslides, especially due to heavy rainfall from climatic changes. Addressing the need for comprehensive assessments, the study integrates multiple parameters such as slope, lithology, internal relief, land cover, and rainfall intensity using publicly accessible datasets such as digital elevation models (DEM), geological maps, and rainfall records. The first comprehensive landslide susceptibility map for Türkiye was produced, validated through histogram and ROC curve analyses, resulting in 1:500,000 scale maps for each basin. This assessment offers a detailed understanding of landslide occurrences, identifying that moderate slopes (5°-15°) and internal relief (200–250 m/km²) significantly influence landslides. A potential threshold for the Topographic Wetness Index (TWI) at 12–13 was also identified. The study highlights the importance of DEM resolution and strategic area subdivision for detailed analysis. The maps and classification methods provide a valuable framework for future research, enhancing understanding of geological hazards and aiding decision-makers in managing landslide risks. Recommendations include prioritizing open data, evaluating TWI, and refining classification methods for improved accuracy. Landslide Susceptibility Semi-Quantitative Analytical Hierarchy Process (AHP) Drainage Basin Scale AHP-GIS Integration Türkiye Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Vulnerable rural and mountainous regions are disproportionately affected by landslides, a major geohazard that causes significant human and economic costs worldwide. The increasing impacts of climate change, including increased precipitation and extreme weather events, are exacerbating the frequency and severity of landslides, particularly in Türkiye's extensive mountainous regions (Okalp and Akgün 2022 ). Most landslides in Türkiye, which often coincide with floods, are associated with heavy rainfall events, which are further complicated by climate change-related weather anomalies. The Center for Research on the Epidemiology of Disasters (CRED) reports that landslides are responsible for at least 17% of natural disaster casualties worldwide, a proportion that is expected to increase as the effects of climate change intensify (Lacasse et al. 2010 ). Since the mid-20th century, the Disaster and Emergency Management Authority (AFAD) in Türkiye has been methodically documenting landslide damage. These records show that there have been 23,041 landslides since 1950, causing over 700 deaths and affecting 5,472 communities, with the risk increasingly exacerbated by climate change (Gökçe et al. 2008 ; AFAD 2018 ; Fidan 2019 ). In response to this threat, AFAD has supported the relocation of 59,345 people to safer areas between 1950 and 2008 (Gökçe et al. 2008 ). Although numerous studies have outlined various methods for assessing landslide hazard, there remains a significant gap in the thorough assessment of landslide susceptibility over large geographic areas, such as countries in their entirety (Guzzetti 2000 ; Yoshimatsu and Abe 2006 ; Abella and van Westen 2007 ; Sabatakakis et al. 2013 ; Gaprindashvili and van Westen 2016 ). This gap highlights the critical need to broaden research perspectives from local to national and continental scales, which would increase the robustness and relevance of landslide susceptibility indices. In the context of Türkiye, localized studies, such as those conducted in Azdavay, Kastamonu, and Rize, have provided invaluable insights and methodological contributions to the field (Ercanoglu and Temiz 2011 ; Reis et al. 2012 ). However, the need for comprehensive, nationwide assessments remains paramount for a holistic understanding of landslide susceptibility across Türkiye, which is critical for the development of effective landslide mitigation and management strategies. The development of a landslide susceptibility map for Türkiye using a qualitative approach by Okalp and Akgün ( 2016 ) represents a significant advance in understanding the country's geologic hazards. This research has extended previous efforts by using the Analytical Hierarchy Process (AHP) to meticulously assess landslide susceptibility and identify vulnerable regions across Türkiye, thereby providing a comprehensive analysis for the assessment of landslide risks in the country (Okalp 2013 ). It is believed that the study's comprehensive and detailed approach significantly expanded the field of landslide susceptibility assessment and provided a reliable and effective framework for future research and assessment efforts, which are critical in ensuring community safety and resilience in the frame of climate change challenges. This research utilizes a GIS-based, semi-quantitative approach, known as the Analytical Hierarchy Process (AHP), to evaluate landslide susceptibility within major drainage basins across Türkiye. The AHP was selected for its ability to integrate multiple parameters, facilitating the production of robust landslide susceptibility maps. Publicly accessible, medium-scale datasets were utilized for the regional analysis. After rigorous validation, an innovative synthetic classification procedure was developed to delineate susceptibility zones. This research attempts to represent a significant advance in the assessment of landslide susceptibility for all major Turkish catchments. The described method offers decision-makers a useful framework for assessing landslide susceptibility, hazard, and risk at regional scales, with potential applications at national or continental levels. Study region Occupying a strategic position at the crossroads of Europe and Asia, Türkiye's 783,562 km² landmass encompasses the Anatolian Peninsula and eastern Thrace. The Black Sea, Mediterranean Sea, Aegean Sea, and Marmara Sea form an extensive 7,200 km coastline (CIA World Factbook 2002). Anatolia, a vast semi-arid plateau surrounded by coastal mountains, makes up about 97% of Türkiye's territory. Eastern Thrace, the remaining 3% of the landmass, is separated by the Bosphorus, the Dardanelles, and the Sea of Marmara. This European part is home to over 10% of Türkiye's population (TUIK 2012 ). Towering over the eastern border is Mt. Ararat, the country's highest peak at 5,137 meters. A recent surge in geological data collection has illuminated Türkiye's complex geological history. This complexity is prominently reflected in the diverse geological features found throughout the Anatolian Peninsula. In particular, the region, which is bordered by several seas, displays formations that span a wide range of time periods. The Black Sea, is an ancient oceanic backarc basin that has originated in the Cretaceous period. In contrast, the younger Aegean Sea began its development during the Oligo-Miocene epoch (Garfunkel 2004 ). Türkiye, which ranks tenth in the world in terms of seismic activity, experiences a complex tectonic environment driven by the Aegean Arc and the North Anatolian Fault Zone (Bayrak et al., 2008). The geology of the Black Sea is categorized into three distinct tectonic divisions: the Pontides, the Anatolides-Taurides, and the Arabian Platform, each characterized by its own geological narrative (Okay 2008 ). The Pontides area, situated to the north of the İzmir-Ankara-Erzincan suture, displays the impact of the Alpide orogeny, a mountain-forming event, as seen in its folded and thrust-faulted structures. In contrast, the Anatolide-Tauride terrane in southern Türkiye is marked by extensive deformation and regional metamorphism, also due to Alpide orogenic activity (Monod et al. 2003 ). The region of Southeast Anatolia, which extends from the Arabian platform, was previously separated from the Anatolide-Tauride by the southern branch of the Neo-Tethys ocean, a division still evident in the Assyrian suture (Şengör and Yilmaz 1981 ). During the Oligo-Miocene epoch, a tectonic consolidation occurred as these terranes merged. This era was characterized by continental sedimentation, calcalkaline magmatism, extensional forces, and strike-slip faulting, all contributing to the formation of Türkiye's present geological features and active structures (Okay 2008 ). Türkiye has a mosaic of climatic zones due to its diverse geography. The Aegean and Mediterranean coasts experience a classic Mediterranean climate characterized by hot, dry summers and mild, wet winters. In contrast, the Black Sea coast receives significantly more precipitation, especially in the east (up to 2200 mm annually) (Şensoy et al. 2013 ), resulting in a temperate oceanic climate. The Sea of Marmara features a transitional climate, with occasional snowfall during winter. Encircled by mountains, Central Anatolia is subject to a continental climate, characterized by severe winters. In the eastern regions, temperatures can vary between − 30°C and − 38°C, whereas in the western areas, the average winter temperature remains below 1°C. Summers in Central Anatolia are typically hot and dry, with temperatures frequently rising above 30°C. The national average annual precipitation is approximately 640 mm, exhibiting a slightly decreasing trend (Şensoy et al. 2013 ). The Konya and Malatya plains represent the driest areas, receiving less than 300 mm of rainfall each year. Landslide susceptibility mapping has become more sophisticated over time. Early methods have simply overlain geologic and topographic features on existing landslide locations to identify areas at risk. Today, researchers use advanced techniques such as statistical analysis (bivariate and multivariate), logistic regression, the Analytical Hierarchy Process (AHP), fuzzy logic, and even artificial neural networks. This study has utilized AHP due to its ability to minimize subjectivity and ensure consistent judgments in the assessment of landslide susceptibility. Hydrological analyses were employed to assess landslide susceptibility in major Turkish drainage basins, each distinguished by its unique climatic, landform, and seismic features. The Analytical Hierarchy Process (AHP) with a spatial resolution of 90 meters and an intermediate scale of 1:500,000, incorporating ten causal factors was performed. Following the analysis, the two least influential factors were excluded for re-evaluation, ensuring a comprehensive and reliable susceptibility assessment for each basin (Okalp 2013 ). Methods Although landslide susceptibility assessment has received considerable attention over the last two decades, consensus on a standardized methodology remains elusive (Ercanoglu and Gokceoglu 2004 ). A variety of qualitative and quantitative approaches can be used to produce landslide susceptibility maps (Soeters and van Westen 1996 ). Qualitative methods, such as those based on landslide inventories and heuristic analysis, often incorporate subjective expert knowledge and experience. While some methods introduce semi-quantitative aspects through ranking and weighting of observed landslide events, subjectivity remains a core component. Conversely, quantitative methods, including statistical and deterministic techniques, strive for greater objectivity by emphasizing data-driven analysis rather than expert opinion (Soeters and van Westen 1996 ). Landslide susceptibility mapping relies on several approaches, each with different characteristics. Landslide inventory, a basic technique, uses the spatial distribution of landslide deposits to directly generate susceptibility maps. Heuristic analysis uses geomorphic analysis and qualitative map combination, relying on expert judgment and thematic map weighting. Statistical approaches, including bivariate and multivariate analysis, integrate factor maps with landslide distribution data. Weighting values are assigned based on the spatial correlation and the occurrence or non-occurrence of landslides within specified land units. Deterministic methods, which necessitate comprehensive geotechnical and hydrological data, are well-suited for large-scale analyses in regions with uniform geomorphic and geological conditions. The selection of an appropriate method for assessing landslide susceptibility depends on the scale of the analysis (Soeters and van Westen 1996 ). In the context of national-level assessments, where susceptibility levels may not warrant the extensive time investment required by highly detailed methods, a semi-quantitative approach offers a valuable compromise. This study uses a semi-quantitative approach implemented within the ArcGIS Spatial Analyst extension. This method involves organizing relevant criteria into a “criteria tree” structure. Each criterion is then standardized, weighted, and subsequently integrated to produce one or more “composite index maps”. Notably, this approach is based on Saaty’s ( 1980 ) Analytical Hierarchy Process (AHP). Recent applications of AHP in landslide susceptibility assessment have underscored its effectiveness in decision-making scenarios (Saaty and Vargas 2012 ). Landslide susceptibility assessment using the AHP The Analytical Hierarchy Process (AHP), developed by Saaty ( 1980 ), provides a powerful quantitative framework for multi-criteria decision making. A flexible and comprehensive tool for the analysis of complex problems, AHP facilitates the structuring of complexity and systematic judgment, making it particularly well suited to scenarios involving multiple, often conflicting, criteria. AHP is characterized by its method of deriving ratio scale priorities or weights rather than arbitrary assignments (Yalcin 2008 ). This is achieved by constructing a hierarchical problem structure and making pairwise relative comparisons. This approach effectively merges objective and subjective elements into the decision-making process, offering a solid and transparent framework. Grounded in the principles of decomposition, comparative judgment, and priority synthesis (Malczewski 1999 ), the Analytical Hierarchy Process (AHP) uses a structured approach to decision making. This includes hierarchical problem organization, pairwise comparisons, incorporation of redundant judgments for consistency, the eigenvector method for weight derivation, and thorough consistency analysis. The effectiveness of AHP in landslide susceptibility assessment is well established, with a substantial body of research demonstrating its successful application (Chung and Leclerc 1994 ; Barredo et al. 2000 ; Ayalew and Yamagishi 2005 ; Komac 2006 ; Akgün and Bulut 2007 ; Yalcin 2008 ). This study employs the Analytical Hierarchy Process (AHP) to systematically assign preference scores to various factors influencing landslide susceptibility. The approach follows Saaty's (2000) proposal, which uses a numerical relational scale to facilitate pairwise comparisons between attributes (i.e., layer classes or parameters). A key strength of AHP is its ability to assess the internal consistency of these pairwise assessments. Saaty ( 2000 ) demonstrated that a consistent reciprocal matrix - where comparisons are transitive - yields a maximum eigenvalue (λ max ) equal to the number of comparisons (n). Based on this concept, a consistency index (CI) is calculated and a consistency ratio (CR) is derived (Saaty 1977 ). CR values less than or equal to 10% indicate acceptable consistency. Judgments above this threshold require revision to ensure robust and reliable weightings within the AHP framework. The Landslide Susceptibility Index (LSI) is calculated utilizing weights and scores derived from the eigenvalues of AHP matrices. These matrices elucidate the relationships between various influencing factors and their respective classes. After obtaining LSI values, categorization into different landslide susceptibility classes provides the final landslide susceptibility zonation map (Okalp 2013 ; Okalp and Akgün 2022 ). Factor selection While various landslide susceptibility studies include a wide range of factors - both natural (i.e., lithology, lineament) and artificial (i.e., roads), causal (i.e., slope, lithology) and triggering (i.e., rainfall, seismicity) - this study adopts a focused approach for greater clarity and efficiency. Although it is possible to include numerous factors, as demonstrated in discriminant models (Guzzetti et al. 1999 ), a focused approach can offer advantages in analysis and interpretation. In this study, ten key factors were carefully selected, covering both causal elements (i.e., slope, internal relief and lithology) and triggering mechanisms (i.e., rainfall intensity and earthquake). This targeted selection aimed to provide a comprehensive, yet manageable assessment of the drivers of landslide susceptibility. The selection of factors in this study prioritized both data accessibility and established importance in landslide hazard research. A resolution of 90 m x 90 m was considered appropriate for the assessment of landslide hazard in Türkiye at a scale of 1:500,000 using the publicly available CSI-SRTM DEM version 4.1 published at the CGIAR-CSI website (Jarvis et al. 2008 ). The importance of a high-quality Digital Elevation Model (DEM) must be emphasized, as it is the basis for deriving most of the factors investigated (Okalp 2013 ). Calculations for slope angle and internal relief were performed using methods proposed by Hickey ( 2000 ) within the Spatial Analyst extension of ArcGIS. A 11 x 11 pixel window was used for the SRTM DEM used. Additional DEM derivatives were calculated within the SAGA GIS environment. The aspect layer, which identifies the direction of slope, was generated at a resolution of 90 meters. The Topographic Wetness Index (TWI), a causal factor used to analyze spatial scale effects on hydrologic processes, was calculated based on local upslope area and local slope (Beven and Kirkby 1979). The Topographic Position Index (TPI), which compares the elevation of a cell to the average elevation of the surrounding area, was calculated using SAGA GIS, producing a layer with a resolution of 90 meters. Similarly, profile and plan curvature, which influence water flow dynamics, were analyzed in SAGA GIS and included as causal factors at 90 m resolution (Okalp 2013 ). The land use map was derived from the seamless vector data provided by CORINE Land Cover 2018 (CLC 2020 ). This robust dataset, harmonized for the 2018 reference year, used high-resolution imagery from the Landsat ETM + and Sentinel-2 satellites. The comprehensive hierarchical classification scheme included 44 land cover subclasses, structured into three levels. To optimize the dataset for landslide susceptibility assessment, a synthetic classification procedure was implemented that integrated the original CORINE Level 3 subclasses (Okalp 2013 ). Lithological data for this study were sourced from geological maps at a scale of 1:500,000, published by the General Directorate of Mineral Research and Exploration (MTA) of Türkiye. After digitization, these lithological units were reclassified into a more manageable set of 24 synthetic classes as described in Okalp ( 2013 ). Earthquake data layers were integrated through detailed digitization of the 2018 Türkiye Earthquake Map produced by the Disaster and Emergency Management Authority (AFAD). This map represents the latest government-approved knowledge of seismic activity in the country. The map classifies Peak Ground Acceleration (PGA) with a 10% probability of being exceeded in 50 years into five distinct zones delineated by specific thresholds (0.10 g/0.167 g/0.33 g/ 0.50 g). Rainfall data, essential for landslide susceptibility analysis, were derived from meteorological station records spanning a robust 65-year period. Monthly total rainfall measurements were aggregated to produce annual mean total rainfall values. The inverse distance weighting (IDW) method was used to spatially interpolate these values which resulted in a continuous rainfall map with a fine-grained resolution of 90 m pixels, suitable for detailed analysis within the study area (Okalp 2013 ). Landslide susceptibility mapping has advanced considerably from the initial qualitative methods, which involved overlaying geological and morphological slope characteristics onto landslide inventories (Nielsen et al. 1979). Contemporary assessments employ sophisticated quantitative techniques such as AHP, bivariate analysis, and others (Carrara 1983 ; van Westen 1997 ; Dai et al. 2001 ; Lee and Min 2001 ; Ercanoglu and Gokceoglu 2004 ; Lee et al. 2004 ; Komac 2006 ). The Analytical Hierarchy Process (AHP) was used in this research owing to its ability to synthesize various parameters. The strength of AHP lies in its ability to establish correlations between factors within a structured framework, which facilitates the generation of more reliable and consistent landslide susceptibility maps. The management and analysis of high-resolution geospatial data for nationwide landslide susceptibility assessment poses a significant computational challenge. To achieve a high-resolution landslide susceptibility map of Türkiye, a reduction in dataset file sizes was crucial. This study adopted a segmentation approach that divided Türkiye into meaningful geomorphologic zones, specifically drainage basins. Drainage basins serve as natural topographic units defined by a network of tributaries converging on a main outlet and bounded by ridge lines. This segmentation strategy allowed for more efficient data processing and facilitated subsequent landslide susceptibility analysis. In order to optimize the analysis and achieve detailed spatial results, Türkiye was segmented into its 26 major drainage basins. Each basin was individually modeled with a fine spatial resolution of 90 meters and an appropriate scale of 1:500,000 for assessing landslide susceptibility. Initially, the Analytical Hierarchy Process (AHP) was applied to each basin, incorporating a set of ten influential factors. Rigorous post-analysis evaluation identified the two least influential factors - aspect and rainfall intensity. These were subsequently excluded and each basin was recalculated using an optimized set of eight factors. The systematic use of AHP with both the initial and refined factor sets produced results for each basin, including comparative graphs and Receiver Operator Characteristic (ROC) curves. Finally, these zonal results were integrated to produce a comprehensive and highly detailed landslide susceptibility map of Türkiye. Visual representations of the major basin boundaries are shown in Fig. 1 and historical Landslide Area Ratios (LAR) of the basins are summarized in Table 1 . Table 1 Historical Landslide Area Ratios (LAR) of basins (Okalp 2013 ) No Basin Name Landslide Area (A; km 2 ) Basin Area (B; km 2 ) LAR (A/B; %) 1 Lower Maritsa-Ergene 5.320 14464.814 0.04 2 Marmara 436.866 23113.869 1.89 3 Susurluk 143.094 24293.385 0.59 4 North Aegean 53.812 9952.334 0.54 5 Gediz 84.900 16976.216 0.50 6 Kucuk Menderes 5.805 7029.945 0.08 7 Buyuk Menderes 209.229 26010.286 0.80 8 Western Mediterranean 299.530 21084.725 1.42 9 Antalya 141.059 20213.399 0.70 10 Burdur Endorheic 9.117 6273.775 0.15 11 Akarcay Endorheic 223.518 7954.481 2.81 12 Sakarya 1077.202 63256.975 1.70 13 Western Black Sea 3060.300 28967.667 10.56 14 Yesilirmak 1734.562 39614.187 4.38 15 Kizilirmak 1652.320 82100.076 2.01 16 Konya Endorheic 24.241 49805.341 0.05 17 Eastern Mediterranean 484.859 21657.879 2.24 18 Seyhan 58.305 22135.942 0.26 19 Lower Asi 27.280 7856.754 0.35 20 Ceyhan 165.300 21487.635 0.77 21 Upper Euphrates 6058.294 121677.479 4.98 22 Eastern Black Sea 665.532 22852.488 2.91 23 Upper Coruh 1014.108 20251.609 5.01 24 Upper Aras 1276.114 28099.522 4.54 25 Lake Van Endorheic 373.357 17916.731 2.08 26 Upper Tigris 1304.693 54278.689 2.40 Results In this section, a meticulous analysis has been performed for each study region based on its drainage basin delineation, with individual results presented for greater clarity and understanding. Subsequent subsections provide a comprehensive overview of the validation process for the formulated landslide susceptibility maps, using ROC curves to ensure increased accuracy and reliability. A thorough explanation of the selection methodology is provided, including both eight- and ten-factor-based approaches to ensure a robust and systematic analysis. An explanation of the synthetic classification process is also presented. This process is critical to the optimal selection and categorization of maps, providing a solid foundation for further discussion and evaluation, and reinforcing the comprehensive and methodological approach of this study. Drainage basin metrics for historical landslides A detailed comparative analysis of the historical landslide metrics has been carried out to identify both unique and common characteristics that contribute to landslide susceptibility in the different basins of Türkiye. This comprehensive analysis has highlighted not only the significant variations in geomorphic and hydrologic parameters that have historically influenced landslide occurrence, but also the constants that tie these diverse regions together. In particular, the seismic zones, especially zones 1 and 2, show a significant correlation with landslide incidence, highlighting the influence of seismic activity on slope destabilization. The morphological typology of slope curvature and landform classes across basins accentuates the susceptibility of specific features, with planar slopes and open landforms consistently identified as landscapes of increased susceptibility. A common pattern is observed in the slope angles associated with landslides, predominantly between 5° and 10°, although basins such as Buyuk Menderes and Western Mediterranean reflect a slight increase in this range from 10° to 15°. The convergence of human activities and the natural environment is captured by the land cover analysis, which shows that agricultural land and forest areas affected by land use change are often involved in landslide events. This combination highlights the critical need for careful land management practices in landslide-prone regions and demonstrates the complex relationship between anthropogenic factors and natural susceptibility. The integration of geomorphologic findings with climatologic data reveals a robust correlation between precipitation distribution and landslide occurrence. The majority of landslide events occur within well-defined rainfall ranges. This finding is supported by the uniformity of the Topographic Wetness Index (TWI) values, which predominantly range from 12 to 13. Likewise, the internal relief, which varies from 50 m/km² to 200 m/km², shows a significant relation with the occurrence of landslides. These results underscore the predictive power of hydrological and topographic characteristics in identifying areas of increased landslide susceptibility. A review of the metrics across basins shows remarkable resemblances. For example, the Lower Maritsa-Ergene and Marmara basins, despite their different elevations and activity levels, have closely related rainfall and TWI values, suggesting similar hydrological influences. In the same way, the North Aegean and Susurluk basins, with their similar elevation ranges and internal relief values, show the presence of similar topographic features that may affect landslide susceptibility. If this analysis is to be extended, other basins such as the Western Mediterranean, Antalya and Seyhan show moderate landslide activity influenced by a combination of geological formations and anthropogenic land cover types. The Eastern Black Sea and Upper Tigris basins stand out for their high landslide activity, characterized by wide elevation ranges and significant annual precipitation, highlighting the strong influence of climatic conditions. The Eastern Mediterranean and Upper Euphrates Basins, with moderate activity and particular lithological compositions, contribute to the complex landscape of landslide activity. In sharp contrast, the Lower Asi and Konya Endorheic Basins exhibit remarkably low landslide activity. This observation highlights the critical role of land management practices in maintaining slope stability, particularly in agricultural and semi-natural landscapes. Grouping basins according to common characteristics provides invaluable insights for better risk management. The North Aegean, Gediz, and Lower Maritsa-Ergene basins share very low to low landslide activity and landslide-prone geological formations, mainly continental clastic rocks, forming a common susceptibility profile. Conversely, the Marmara Basin, which has some similar lithological features, reflects moderate activity, suggesting the role of location and various factors such as rainfall in affecting landslide activities. These similarities and contrasts between basins emphasize a strategic perspective for landslide risk management, pointing towards regions where similar mitigation strategies may be effective. It also provides a framework for prioritizing research and interventions based on common risk profiles to increase the effectiveness of landslide mitigation. The following comprehensive Table 2 serves as an essential reference for a deeper, basin-by-basin discussion that will explore these dynamics, aid in identifying targeted intervention points for each basin, and enrich the collective understanding of the multifaceted nature of landslide susceptibility in Türkiye. Table 2 Major Drainage Basin Characteristics for Historical Landslides in Türkiye Basin Name (No) Elevation Range (m) Landslide Activity Lithologies Prone to Landslides Rainfall Range (mm) (in general) TWI Values Range (in general) Internal Relief Range (m/km²) (in general) Lower Maritsa-Ergene (1) 0–1021 Notably low Clastic and carbonate rocks, continental clastic rocks 496–798 (600–700) 0–22 (12 and 13) 0–355 (50–150) Marmara (2) 0–1538 Moderate Continental clastic rocks, clastic and carbonate rocks 533–1263 (600–800) 9–23 (12 and 13) 0–700 (50–150) Susurluk (3) 0–2529 Low Continental clastic rocks 435–834 (600–700) 8–23 (12 and 13) 0–806 (100–200) North Aegean (4) 0–1759 Very low Undifferentiated volcanic rocks 518–946 (600–700) 0–22 (12 and 13) 0–710 (150–200) Gediz (5) 0–2298 Very low Continental clastic rocks 446–1044 (400–500) 8–23 (12 and 13) 0–856 (150–200) Kucuk Menderes (6) 0–2134 Very low Carbonate rocks 483–926 (700–800) 9–23 (12 and 13) 0–804 (100–150) Buyuk Menderes (7) 0–2519 Low Continental clastic rocks 421–1213 (500–700) 8–23 (12 and 13) 0–827 (150–200) Western Mediterranean (8) 0–3039 Moderate Clastic and carbonate rocks, continental clastic rocks 420–1317 (500–700) 8–22 (12 and 13) 0–1044 (200–300) Antalya (9) 0–2972 Low Clastic and carbonate rocks 300–763 (700–800) 0–23 (12 and 13) 0–1255 (200–300) Burdur Endorheic (10) 821–2738 Very low Continental clastic rocks, ophiolitic rocks 488–887 (500–600) 8–23 (12 and 13) 0–901 (200–300) Akarcay Endorheic (11) 949–2576 High Continental clastic rocks 314–621 (500–600) 9–23 (12 and 13) 0–749 (150–200) Sakarya (12) 0–2460 Moderate Clastic and carbonate rocks 286–1319 (300–400) 8–23 (12 and 13) 0–837 (150–200) Western Black Sea (13) 0–2397 Very high Clastic and carbonate rocks 382–1275 (800–1000) 0–23 (12 and 13) 0–970 (200–300) Yesilirmak (14) 0–3288 High Continental clastic rocks, clastic and carbonate rocks 346–1108 (400–600) 8–23 (12 and 13) 0–822 (150–250) Kizilirmak (15) 0–3857 High Clastic and carbonate rocks 221–845 (400–500) 8–23 (12 and 13) 0–772 (150–200) Konya Endorheic (16) 899–3405 Very low Continental clastic rocks 263–1113 (400–500) 8–23 (12 and 13) 0–862 (100–150) Eastern Mediterranean (17) 0–3487 Moderate Continental clastic rocks 370–700 (500–600) 8–23 (12 and 13) 0–1395 (200–250) Seyhan (18) 0–3683 Very low Clastic and carbonate rocks, continental clastic rocks 316–1062 (300–600) 8–23 (12 and 13) 0–1437 (200–250) Lower Asi (19) 0–2201 Very low Clastic and carbonate rocks 493–980 (700–900) 8–23 (12 and 13) 0–1262 (100–150) Ceyhan (20) 0–3058 Low Clastic and carbonate rocks 317–1628 (600–800) 8–23 (12 and 13) 0–1147 (100–150) Upper Euphrates (21) 317–3838 High Continental clastic rocks 254–1259 (400–600) 8–23 (12 and 13) 0–1114 (150–200) Eastern Black Sea (22) 0–3776 High Volcanic and sedimentary rocks 401–2594 (900–1100) 8–23 (12 and 13) 3–1102 (200–300) Upper Coruh (23) 53–3893 High Clastic and carbonate rocks 311–2038 (400–500) 8–24 (12 and 13) 4–1027 (200–300) Upper Aras (24) 792–5100 High Continental clastic rocks 227–778 (400–600) 8–23 (12 and 13) 0–1961 (150–250) Lake Van Endorheic (25) 1638–4029 High Clastic and carbonate rocks, continental clastic rocks 358–912 (400–500) 8–23 (12 and 13) 0–753 (150–200) Upper Tigris (26) 333–3935 High Clastic and carbonate rocks 299–1887 (900–1000) 8–24 (12 and 13) 0–1293 (150–300) Utilizing the Analytic Hierarchy Process Within the AHP framework, pairwise comparison matrices were carefully constructed for each study basin. Factor weights were subsequently computed for both the eight-factor and ten-factor methods, as detailed in Table 3 . To rigorously evaluate the appropriateness of the assigned scores, a quantitative analysis was performed considering the distribution of historical landslide occurrences across data layer categories. This analysis is summarized in Table 4 , which shows the percentage of pixel counts for each category. Figure 2 provides a visual representation of the resulting unclassified landslide susceptibility maps for each basin generated using both of the factor sets. The overlay of historical landslide polygons on each causal factor layer allowed for the extraction of relevant data values, facilitating an objective evaluation of the assigned rating values. Each factor was segmented into subclasses, and linear normalization (min-max feature scaling) was applied to standardize values within a range of 0 to 1. Aggregating the accumulated layer parameters for both the eight- and ten-factor models derived two distinct landslide susceptibility maps, as visually depicted in Fig. 2 . Notably, the pixel values within these maps spanned the entire spectrum from 0 to 1, providing a comprehensive representation of landslide susceptibility within the respective basins. Table 4 Distribution of the historical landslide areas index (percent of pixel) in regards to various data layer classes for the Western Mediterranean basin (please refer to the details of the remaining 25 drainage basins in the Supplementary Material Section as Online Resource 2) Data layer Class LAI Data layer Class LAI Slope 5° − 10° 18.40 Lithology Young deposits 8.00 10° − 15° 50.51 Basalt 4.23 15° − 20° 25.37 Gabbro 0.11 20° − 25° 4.23 Continental clastic rocks 20.23 25° − 30° 0.69 Carbonate rocks 1.94 30° − 35° 0.69 Clastic and carbonate rocks 29.49 35° − 40° 0.11 Limestone 7.20 Internal relief 50–100 0.34 Marble 0.23 100–150 3.89 Metamorpic rocks 1.03 150–200 18.29 Ophiolitic rocks 27.54 200–250 29.14 Aspect N 1.03 250–300 23.77 NE 6.63 300–350 14.40 E 15.09 350–400 5.26 SE 18.06 400–450 2.97 S 13.03 450–500 0.46 SW 14.74 500–550 0.69 W 19.89 550–600 0.46 NW 11.54 600–650 0.11 Landform Canyons, deeply incised streams 3.20 650–700 0.23 Midslope drainages, shallow valleys 4.11 Rainfall 500–600 32.11 Upland drainages, headwaters 0.57 600–700 29.83 U-shaped valleys 20.91 700–800 20.34 Plains 0.34 800–900 17.37 Open slopes 68.23 900–1000 0.23 Upper slopes, mesas 2.29 1000–1100 0.11 Local ridges/hills in valleys 0.23 TWI 10–11 1.37 Midslope ridges, small hills in plains 0.11 11–12 17.26 Curvature V / V 1.37 12–13 45.03 V / S 11.77 13–14 29.94 V / X 0.69 14–15 5.37 S / V 10.17 15–16 1.03 S / S 73.71 Land cover Agricultural areas 42.74 S / X 1.14 Forest 44.34 X / V 0.11 Semi natural areas 12.91 X / S 0.91 Earthquake Zone 1 67.31 X / X 0.11 Zone 2 32.69 Discussion Landslides are a significant natural hazard within the geographical boundaries of Türkiye. Several contributing factors, such as channel incision, seismic activity, heavy precipitation, and anthropogenic influences, collectively underscore their importance as recurrent events. For the purpose of conducting AHP-based studies, as elaborated in previous sections, a careful selection of causal and triggering factors has been undertaken. This study has assessed various factors deemed important for initiating landslides, including lithology, land cover, internal relief, slope, aspect, classified landforms, classified curvature, and topographic wetness index (TWI). At the same time, rainfall and seismic activity were identified as critical triggering factors, providing the basis for the extensive analysis conducted herein. The rationale for choosing a total of ten factors in the study was two-folds. First, these factors were chosen because of their widespread availability in the public domain and their established utility in landslide susceptibility research efforts. Second, to allow for a detailed and nuanced analysis, the selected factors were categorized into two distinct groups: one set of ten factors and another set of eight factors. Due to their minimal impact on landslide development in areas of moderate susceptibility, aspect and rainfall were deliberately excluded from the ten-factor analysis. In particular, aspect was found to play a relatively minor role in influencing landslide susceptibility throughout the study basins. In addition, the precipitation factor, which denotes the annual mean total precipitation, represents the arithmetic mean of the annual recorded precipitation. While it provides insight into the annual precipitation received, it lacks the granularity necessary to measure rainfall intensity relative to the threshold for potential landslide occurrence. Extreme rainfall events with the potential to trigger landslides may not be accurately captured by the arithmetic averaging of annual rainfall data. In this context, a careful landslide hazard assessment was carried out for each drainage basin using the AHP methodology. Two different approaches, namely the 8-factor and 10-factor models, were used to comprehensively investigate the susceptibility within each basin. Each basin was thoroughly examined and individual results are presented in Online Resource 3, as previously detailed by Okalp ( 2013 ) and Okalp and Akgün ( 2022 ). Landslide inventory maps, crucial for validating the study's findings, were sourced from 1:500,000 scale maps published by the General Directorate of Mineral Research and Exploration of Türkiye (MTA) within the past decade. These authoritative maps were digitized to delineate historical landslide polygons. The polygons were then systematically overlain on the generated landslide susceptibility maps, which incorporated either eight or ten factors. Finally, for each susceptibility map, a comprehensive analysis of pixel counts, both within and outside the landslide areas, was performed to facilitate consistent evaluation. Figure 3 a illustrates the distribution of landslide susceptibility in the western Mediterranean basin under two scenarios: using eight-factor and ten-factor based maps. The figure shows pixel counts categorized as inside and outside historical landslide polygons. The vertical axes represent these pixel count distributions. A key challenge is the disparity in the range of pixel count values between the inner and outer landslide areas. This makes it difficult to visualize both distributions in a single figure. To address this issue, the peak locations for each scenario (eight and ten factors) were highlighted on a unified horizontal line labeled ''E-line'' in Fig. 3 a, as suggested by Okalp ( 2013 ) and Okalp and Akgün ( 2022 ). While the peaks themselves may differ, this approach facilitated a comparative analysis of the overall distribution patterns. After generating two landslide susceptibility maps - one based on eight factors and the other based on ten factors - a critical challenge emerged in the selection of an optimal map for the specific basin studied. Ideally, the most appropriate map would be reflected by the corresponding histogram curves. The ideal histogram for pixel values within historical landslide polygons would have a pronounced positive skew, with its peak (on the x-axis) approaching 1. Conversely, the optimal histogram for pixel values outside of the landslide polygons would exhibit a negative skew, with its peak value approaching 0. This established criterion, based on the distribution tails of the histograms, served as the primary basis for the selection of the eight-factor and ten-factor maps (Okalp, 2013 ). Inspection of Fig. 3 a reveals that the peak values of the histograms from both scenarios, derived from pixel counts within historical landslide polygons, were closely aligned. Moreover, the peak value of the eight-factor histogram for areas outside historical landslide polygons showed a slightly stronger negative skew, nearing a value of 0. However, these subtle differences in the histogram curves were deemed insufficient to decisively select the optimal map (eight-factor vs. ten-factor) for the basin under study. To resolve this ambiguity and facilitate data-driven selection, Receiver Operator Characteristic (ROC) curve analysis was incorporated into the evaluation process as a subsequent step (Aditian et al. 2018 ). To objectively select the optimal landslide susceptibility map, a rigorous evaluation using ROC curves was performed. ROC curves provide a comprehensive assessment of model performance across all classification thresholds. The area under the ROC curve (AUC) is utilized as a quantitative indicator of the model's overall accuracy. As shown in Fig. 3 b, a comparative analysis of the ROC curves revealed superior predictive power for the ten-factor model. This was evidenced by the larger AUC associated with the ten-factor ROC curve. In addition, the ten-factor ROC curve showed a trajectory closer to the upper left corner of the graph, indicating a stronger ability to discriminate between landslide and non-landslide pixels (Fawcett, 2006 ). Therefore, based on the robust performance metrics provided by the ROC analysis, the ten-factor model was selected for this particular basin. Landslide susceptibility maps, along with landslide hazard maps, are often reclassified into a manageable number of classes (typically three to five) to facilitate interpretation. However, a significant obstacle for this process in this study was the substantial variability observed in the susceptibility maps produced for each individual basin. This variability precluded the application of generic classification thresholds across all maps. As a result, each map required independent determination of appropriate thresholds and classification schemes. Although several synthetic classification methods were explored, none provided entirely satisfactory results. In particular, the application of popular techniques such as Jenks' natural breaks, the quantile method, and the geometric interval method resulted in inconsistent thresholds, especially for the "very high" susceptibility subclass. Interestingly, the application of these methods identified the western Mediterranean basin as "very high landslide prone", a finding that contradicts the documented moderate landslide activity in the region. This discrepancy underscores the challenges in effectively using standard procedures to manage the synthetic classification of susceptibility maps produced in this research. An innovative and highly subjective methodology was employed to synthetically classify the maps generated in this study (Okalp 2013 , Okalp and Akgün 2022 ). Initially, the peaks from both the inner and outer landslide polygons were aligned on a singular axis (line E) in Fig. 3 a, independent of their actual magnitudes. The peak value (point A) on the histogram curve, representing pixel counts from areas outside the landslide zones, was used as the initial threshold to delineate between the "no" and "low" susceptibility classes. Subsequently, the intersection (point B) of the histogram curves for the outer and inner landslide polygons was established as the secondary threshold, differentiating the "Low" and "Moderate" susceptibility classes. Furthermore, the peak (point C) on the histogram curve for the inner landslide pixels was designated as the tertiary threshold, distinguishing between the "Moderate" and "High" classes. Lastly, the midpoint (point D) between point C and a fixed value of 1.0 was set as the quaternary threshold to separate the "High" and "Very High" susceptibility classes. This complex classification process was systematically applied to categorize the landslide susceptibility of the Western Mediterranean basin, as illustrated in Fig. 3 c. Other basins were evaluated independently, with their corresponding results detailed in tables and graphs presented in the Supplementary Material Section as Online Resource 4. Evaluation of the generated landslide susceptibility maps The Analytic Hierarchy Process (AHP) enables the translation of qualitative concerns into quantifiable metrics, providing an invaluable tool for multi-criteria decision analysis problems. It skillfully transforms subjective judgments into objective data, thereby increasing the robustness of decision processes. In the context of landslide susceptibility analysis, the lack of established or fixed values for weights and ratings corresponding to different factors is further emphasized (Okalp 2013 ). This lack of standardization necessitates the use of objective analysis to determine these values, rather than relying on subjective expert opinion. The Analytic Hierarchy Process (AHP) uses historical landslide footprints to determine rating values and factors influencing landslide occurrence. Overlaying these footprints with unnormalized values for each factor supports a detailed and unbiased assessment, thereby improving landslide hazard analysis. The study revealed several key factors influencing landslide susceptibility across various basins. Slope exhibited a non-linear relationship, with the highest landslide frequencies concentrated between 5° and 15°, indicating a critical range for susceptibility assessments. Moderate internal relief (200–250 m/km²) emerged as a prominent factor associated with increased landslide activity. Notably, historic landslides in the study areas clustered within a specific range of Topographic Wetness Index (TWI) values. A significant proportion occurred within a TWI layer where values of 12 and 13 were extracted from a DEM with a 90 m resolution. Because topography affects water movement in sloping terrain, the TWI effectively quantifies the effect of local topography on hydrologic processes. This provides insight into soil moisture distribution and surface saturation. Incorporation into the TOPMODEL which is a distributed hydrological model that aids in defining hydrological similarity, highlights the importance of TWI in modeling topography-driven processes at hillslope and basin scales. Further analysis showed that about half of the basins possessed TWI values of 12–13, which fell at the midpoint of the TWI distribution. This suggested a potential threshold of TWI 12 for landslide initiation within the study basins at this resolution. As expected, historic landslides occurred predominantly in areas with open and planar slope (S/S) curvature types. However, the lack of significant aggregation across precipitation and aspect distributions within the study regions necessitated the use of the Analytic Hierarchy Process (AHP). This involved a two-pronged analysis for each basin, employing both 8-factor and 10-factor layers. Historical data analysis within the study regions showed that agricultural areas and forests were more susceptible to landslides, potentially exacerbated by land-use changes like deforestation. Additionally, a significant portion of historical landslides have occurred within Earthquake Zone 1, highlighting its role as a triggering factor. Regarding lithology, clastic and carbonate rock formations have been identified as being most prone to landslides due to their extensive history of such events and their inherent geological properties. Rainfall intensity, particularly in specific regions, emerged as a primary trigger for landslides, underlining the crucial impact of precipitation on slope stability. Aspect had a minimal influence on susceptibility across the study basins. Specific landforms like U-shaped valleys and open slopes exhibited a clear association with increased landslide frequency. After producing unclassified landslide susceptibility maps for the basins using both 8-factor and 10-factor approaches, a selection process was carried out as previously described. Comparative analyses showed that the 10-factor approach performed better in 9 out of 26 basins, namely Marmara, Buyuk Menderes, Western Mediterranean, Akarcay Endorheic, Western Black Sea, Yesilirmak, Kizilirmak, Upper Euphrates and Eastern Black Sea basins. The decision between the 8-factor and 10-factor methods was made by analyzing pixel distributions within historical landslide zones (inner and outer) and examining Receiver Operating Characteristic (ROC) curves. The main criterion was to prioritize peaks of the outer landslide histogram closest to 0 and the inner landslide histogram closest to 1, using ROC curve values as a secondary criterion. When histogram data were inconclusive, the area under the ROC curve was reviewed. The results revealed significant variations in ROC curves across the basins. Several basins achieved impressive AUC values, exceeding 0.7, indicating strong performance in predicting landslides. For example, the Lower Maritsa-Ergene Basin exhibited a remarkable AUC of 0.8709, highlighting its exceptional predictive capability. Similarly, the Akarcay Endorheic Basin reached a high AUC of 0.8421, showcasing its effectiveness in landslide susceptibility assessment. Conversely, some basins presented lower AUC values. The Upper Tigris Basin, for instance, yielded an AUC of 0.606, suggesting a need for further investigation or refinement for this basin. Similarly, the Sakarya Basin displayed a lower AUC of 0.5672, indicating that additional data or adjustments could enhance its predictive performance for this basin. The selected unclassified landslide susceptibility maps, developed using the AHP with either 8 or 10 factors, consistently classified the basins into five distinct groups, as outlined earlier. Table 5 summarizes these group distributions. The Analytic Hierarchy Process (AHP) is a recognized technique for assessing landslide susceptibility and serves as a benchmark in current research, particularly when evaluating against machine learning algorithms. A study by Huang et al. ( 2020 ) in Shicheng County, China, assessed various models including heuristic AHP, statistical approaches, and machine learning techniques like Binary Logistic Regression, Multilayer Perceptron, Backpropagation Neural Network, Support Vector Machine, and C5.0 Decision Tree. The results demonstrated the effectiveness of all models, with the C5.0 Decision Tree achieving the highest accuracy with an AUC of 0.868, suggesting a potential for refining AHP-based results (AUC of 0.773) through integrating machine learning, neural networks, fuzzy logic, and other soft computing techniques. Analysis of the "very high" landslide susceptibility zone, as shown in Fig. 4 and summarized in Table 5 , revealed that in most basins the spatial extent of this zone exceeded that of the corresponding historical landslide footprint, highlighting the ability of the method to identify potentially landslide-prone areas beyond known locations. The focus was on the combined area classified as "high" and "very high" landslide susceptibility within each basin (last columns of Table 5 ), as compared to the historical landslide distribution. Ideally, the "very high" susceptibility area should exceed the documented historical landslide footprint for the corresponding basin. While most basins met this expectation, six basins, notably Akarcay Endorheic, Western Black Sea, and Upper Euphrates, showed a smaller "very high" susceptibility zone compared to the historical landslide area. This discrepancy is likely due to the threshold values used for classifying landslide susceptibility maps, which may have underestimated susceptibility in these basins. Though a synthetic reclassification approach could address this underestimation, it falls outside the scope of this study as all basins were classified using the standardized procedure. Table 5 has been expanded to include an additional column displaying the combined area percentage of "high" and "very high" landslide susceptibility zones. Except for the Akarcay Endorheic Basin, the combined area of these susceptibility classes exceeded the historical landslide area for each basin. The minimal difference in the Akarcay Endorheic Basin may be considered negligible for landslide susceptibility assessment at a 1:500,000 scale. Table 5 Synthetically classified landslide susceptibility zone distributions of basins and comparison of synthetically classified highly landslide susceptible zone areas with historical landslides Basin Selected factors Pixel counts Classified landslide susceptibility zones vs Historical LS No Low Moderate High Very high High + Very high Lower Maritsa-Ergene 8 1783136 64.35% 23.22% 10.26% 2.10% 0.07% > 0.04% 2.17% > 0.04% Marmara 10 2764674 31.42% 18.53% 41.49% 6.38% 2.18% > 1.89% 8.56% > 1.89% Susurluk 8 2938688 55.49% 21.84% 10.68% 9.01% 2.98% > 0.59% 11.99% > 0.59% North Aegean 8 1223660 45.55% 23.62% 9.75% 16.98% 4.10% > 0.54% 21.08% > 0.54% Gediz 8 2050888 51.17% 26.51% 16.51% 4.52% 1.28% > 0.50% 5.80% > 0.50% Kucuk Menderes 8 847415 57.04% 31.62% 10.48% 0.73% 0.12% > 0.08% 0.85% > 0.08% Buyuk Menderes 10 3155909 57.03% 18.65% 15.50% 6.56% 2.26% > 0.80% 8.82% > 0.80% Western Mediterranean 10 2552371 55.82% 18.29% 7.95% 14.68% 3.26% > 1.42% 17.94% > 1.42% Antalya 8 2492362 62.63% 16.80% 9.36% 9.50% 1.71% > 0.70% 11.21% > 0.70% Burdur Endorheic 8 724849 45.87% 24.79% 14.49% 10.18% 4.66% > 0.15% 14.84% > 0.15% Akarcay Endorheic 10 932025 60.37% 28.87% 8.78% 1.76% 0.21% < 2.81% 1.97% 1.70% 14.44% > 1.70% Western Black Sea 10 3568596 46.39% 22.41% 12.24% 14.37% 4.59% 10.56% Yesilirmak 10 4863497 42.89% 26.02% 14.92% 10.23% 5.94% > 4.38% 16.17% > 4.38% Kizilirmak 10 9863497 45.84% 21.97% 11.46% 16.62% 4.11% > 2.01% 20.73% > 2.01% Konya Endorheic 8 5928017 64.77% 20.05% 9.97% 4.61% 0.59% > 0.05% 5.21% > 0.05% Eastern Mediterranean 8 2656410 52.36% 22.79% 10.87% 12.15% 1.82% 2.24% Seyhan 8 2655827 55.39% 23.53% 10.44% 8.93% 1.70% > 0.26% 10.63% > 0.26% Lower Asi 8 947267 71.06% 5.98% 8.25% 10.95% 3.76% > 0.35% 14.71% > 0.35% Ceyhan 8 2595952 73.98% 8.68% 9.29% 6.21% 1.84% > 0.77% 8.05% > 0.77% Upper Euphrates 10 14484190 47.05% 20.87% 16.12% 13.13% 2.84% 4.98% Eastern Black Sea 10 2813106 54.85% 23.17% 8.89% 11.31% 1.77% 2.91% Upper Coruh 8 2495302 51.02% 25.96% 6.46% 13.34% 3.22% 5.01% Upper Aras 8 3420905 53.81% 12.91% 12.74% 15.24% 5.30% > 4.54% 20.54% > 4.54% Lake Van Endorheic 8 2210789 41.25% 30.64% 14.17% 9.81% 4.13% > 2.08% 13.94% > 2.08% Upper Tigris 8 6426892 54.19% 14.68% 12.08% 14.87% 4.19% > 2.40% 19.05% > 2.40% Conclusions and recommendations This study has conducted a comprehensive analysis of landslide susceptibility mapping in mid-sized regions using publicly available datasets and a GIS-based semi-quantitative approach. The study systematically outlined an established framework for mapping landslide susceptibility. Using the Analytical Hierarchy Process (AHP), which is an accepted semi-quantitative method, the research applied this technique to the study basins by using both eight- and ten-variable models. The study included rigorous validation and evaluation processes, including histogram and ROC curve analyses, resulting in factor-based maps for each basin. As a result, the research produced accurately constructed 1:500,000 scale landslide susceptibility maps for the designated regions. This work stands as a pioneering effort in systematically assessing landslide susceptibility across the major drainage basins of Türkiye. By encompassing all these basins, the study effectively provides the first comprehensive assessment of landslide susceptibility for the entire country. This basin-wide approach offers a more nuanced understanding of the factors influencing landslide occurrence compared to previous national-scale studies that might rely on less detailed data. The findings not only improved our knowledge of landslide susceptibility in Türkiye but also established an invaluable framework for future susceptibility assessments in other geologically diverse regions. The methodology developed for semi-quantitative landslide susceptibility zoning provided important insights for decision-makers involved in regional-scale assessment of landslide susceptibility, hazards and risks, and is potentially applicable at both national and continental scales. Scale and pixel size are recognized as critical elements in landslide susceptibility analysis. Their selection must prioritize practicality and ensure alignment with the capabilities of available hardware and software. This is particularly important when processing high-resolution datasets covering large areas. Given these constraints, the study identified a 90-meter pixel resolution and a 1:500,000 scale as optimal for investigating landslide susceptibility over large regions. Results indicated that the 8-factor approach produced superior results in 17 of the 26 basins analyzed. A significant number of historical landslides were noted within the Topographic Wetness Index (TWI) layer, particularly at TWI values of 12 and 13, calculated from a 90-meter resolution digital elevation model (DEM). TWI is highly regarded as an important metric for modeling topographic influences at the hillslope or drainage basin scale. Detailed analysis of the distribution of TWI across the basins identified a value of 12 as the critical threshold for landslide initiation at the 90-meter pixel resolution used. In the basin-specific assessments, curvature, landform, and seismic activity emerged as the primary controlling factors, collectively accounting for approximately 50% of the influence on landslide susceptibility (Table 6 ). The curvature and landform layers, which are terrain derivatives, were extracted directly from the DEM. These factors are significantly influenced by lithology, climatic conditions, and seismic activity, and serve as critical indicators of landslide potential. This demonstrates a strong alignment between digital modeling and field-based analytical methods for understanding and predicting landslide activity. Table 6 Weights of factors obtained for basins (governing factors are highlighted in grey color) Basin Slope Int. relief Rainfall Lithology Land cov. Earthq. Aspect TWI Landform Curvatur. Lo. Mar-Erg. 8 12.24% 9.24% - 9.97% 11.11% 9.31% - 11.11% 18.47% 18.55% Lo. Mar-Erg. 10 8.09% 8.09% 14.85% 8.09% 8.09% 8.09% 4.43% 8.82% 15.72% 15.72% Marmara 8 13.17% 7.35% - 8.51% 13.17% 16.91% - 9.29% 15.35% 16.24% Marmara 10 10.67% 6.87% 7.66% 7.66% 12.92% 15.04% 2.83% 8.31% 14.02% 14.02% Susurluk 8 12.03% 5.86% - 12.01% 12.99% 16.39% - 10.13% 14.92% 15.66% Susurluk 10 10.92% 4.91% 8.23% 9.50% 11.99% 14.54% 2.90% 8.84% 13.62% 14.54% Nort. Aegean 8 9.04% 7.25% - 11.67% 11.67% 18.08% - 10.08% 15.40% 16.83% Nort. Aegean 10 7.63% 6.30% 8.81% 9.47% 9.47% 15.81% 5.64% 8.18% 13.44% 15.25% Gediz 8 10.45% 7.74% - 11.42% 12.02% 18.27% - 9.13% 15.48% 15.48% Gediz 10 8.38% 6.53% 10.32% 10.32% 10.32% 16.44% 3.17% 8.38% 13.07% 13.07% K. Menderes 8 12.17% 8.54% - 9.28% 11.13% 18.56% - 10.17% 14.45% 15.70% K. Menderes 10 9.72% 8.09% 9.72% 8.09% 9.72% 16.52% 3.48% 8.09% 13.28% 13.28% B. Menderes 8 9.10% 8.20% - 14.81% 9.10% 17.30% - 9.99% 15.98% 15.52% B. Menderes 10 7.44% 7.44% 6.75% 12.02% 8.66% 16.67% 4.06% 8.01% 14.47% 14.47% West. Med. 8 11.56% 6.43% - 6.43% 11.53% 16.95% - 11.56% 16.69% 18.86% West. Med. 10 10.88% 5.76% 6.48% 5.76% 9.77% 13.44% 4.50% 10.45% 15.89% 17.06% Antalya 8 12.01% 6.16% - 12.01% 12.01% 12.92% - 12.92% 14.75% 17.22% Antalya 10 9.37% 4.93% 14.52% 9.37% 9.37% 10.82% 4.74% 10.07% 11.29% 15.52% Burdur End. 8 10.00% 5.00% - 6.01% 10.00% 22.21% - 10.00% 17.52% 19.27% Burdur End. 10 9.07% 4.62% 9.39% 4.62% 8.70% 17.78% 3.82% 9.96% 15.57% 16.46% Akarcay End 8 7.95% 6.72% - 10.64% 14.58% 15.15% - 11.71% 16.18% 17.07% Akarcay End 10 7.02% 6.18% 11.97% 8.23% 12.74% 12.74% 3.94% 9.50% 13.64% 14.04% Sakarya 8 9.94% 6.28% - 7.57% 13.95% 15.14% - 11.86% 19.14% 16.14% Sakarya 10 8.94% 5.59% 7.42% 6.88% 12.54% 13.39% 3.60% 11.02% 15.31% 15.31% West. Bl. Sea 8 9.12% 5.46% - 19.10% 12.89% 11.80% - 10.81% 14.75% 16.07% West. Bl. Sea 10 8.41% 4.89% 5.94% 14.61% 12.88% 11.57% 3.97% 9.35% 13.77% 14.61% Yesilirmak 8 9.95% 5.37% - 4.64% 10.52% 20.47% - 9.95% 17.84% 21.26% Yesilirmak 10 8.93% 4.57% 8.47% 4.05% 9.33% 17.91% 3.62% 9.75% 16.68% 16.68% Kizilirmak 8 10.76% 6.84% - 6.18% 11.59% 12.70% - 13.91% 19.79% 18.23% Kizilirmak 10 8.71% 5.79% 12.68% 5.18% 9.87% 10.65% 3.20% 11.03% 17.15% 15.75% Konya End. 8 10.40% 9.32% - 9.92% 11.11% 11.05% - 9.92% 18.45% 19.84% Konya End. 8.77% 8.77% 8.77% 8.77% 8.77% 9.56% 4.66% 8.77% 16.11% 17.06% East. Med. 8 12.06% 6.73% - 8.48% 14.17% 14.17% - 11.10% 15.49% 17.79% East. Med. 10 10.40% 5.48% 11.85% 7.09% 11.09% 11.85% 3.89% 9.76% 12.77% 15.80% Seyhan 8 10.94% 6.14% - 10.94% 11.90% 15.70% - 11.90% 13.07% 19.42% Seyhan 10 9.28% 5.12% 8.15% 9.28% 11.17% 13.81% 4.38% 9.97% 11.99% 16.84% Lower Asi 8 7.77% 7.22% - 9.06% 15.13% 19.98% - 8.17% 16.34% 16.34% Lower Asi 10 6.95% 6.56% 7.56% 8.20% 13.61% 17.74% 3.22% 7.23% 14.47% 14.47% Ceyhan 8 8.76% 3.93% - 12.13% 17.52% 14.81% - 10.18% 14.93% 17.74% Ceyhan 10 8.02% 3.68% 4.25% 11.33% 16.04% 14.07% 3.68% 9.05% 14.93% 14.93% Up. Euphrates 8 9.69% 6.18% - 6.78% 10.52% 16.42% - 11.56% 19.42% 19.42% Up Euphrates 10 9.17% 5.72% 5.47% 6.17% 9.77% 14.97% 3.21% 10.53% 17.49% 17.49% East. Bl. Sea 8 9.93% 6.10% - 11.81% 19.08% 9.93% - 11.81% 14.32% 17.03% East. Bl. Sea 10 8.73% 5.49% 6.41% 10.70% 17.00% 9.35% 5.26% 10.07% 13.24% 13.76% Upper Coruh 8 11.84% 6.12% - 9.23% 11.97% 15.49% - 10.88% 15.42% 19.06% Upper Coruh 10 10.07% 5.16% 10.82% 8.31% 10.84% 12.07% 4.75% 9.44% 12.98% 15.56% Upper Aras 8 10.19% 6.10% - 5.29% 10.19% 20.73% - 9.76% 19.59% 18.15% Upper Aras 10 9.06% 5.40% 8.60% 4.50% 8.72% 17.24% 3.48% 9.87% 17.24% 15.90% Lk. Van End. 8 10.81% 6.09% - 5.08% 10.10% 19.48% - 10.10% 20.85% 17.48% Lk.Van End. 10 8.88% 4.84% 10.89% 4.53% 8.88% 16.78% 4.67% 9.86% 15.34% 15.34% Upper Tigris 8 9.10% 4.99% - 11.72% 13.43% 19.79% - 11.72% 14.63% 14.63% Upper Tigris 10 8.23% 4.26% 8.78% 10.10% 11.31% 17.22% 3.35% 10.10% 13.33% 13.33% 8 Basin results for 8-factor based analysis 10 Basin results for 10-factor based analysis The methodology used in this study to evaluate mid-scale landslide susceptibility mapping in mid-sized regions provides a valuable framework for future efforts focused on the development of comprehensive nationwide landslide susceptibility maps. However, direct implementation of sophisticated modeling techniques for national or continental-scale landslide susceptibility assessment, which require larger data sets, finer pixel resolution, and greater computational power, remains impractical due to current hardware and software limitations. These factors were incorporated into a comprehensive analysis that resulted in the development of landslide susceptibility maps for the study regions. The semi-quantitative model employed facilitated the generation of histogram and Receiver Operating Characteristic (ROC) curves. These curves were then subjected to rigorous evaluation and reclassification using a novel methodological approach. This process culminated in the careful construction and robust validation of several landslide susceptibility maps within the study areas. Guided by our findings, we propose the following recommendations for future landslide susceptibility research: Prioritize open data accessibility : This research utilized open-source or publicly accessible datasets. Hard copies of landslide inventory maps, geological maps, and rainfall data were obtained from relevant government agencies and subsequently digitized into vector formats. Additional datasets were sourced from CGIAR-CSI and the European Environment Agency. Utilization of causal factors from DEM : The study utilized the Digital Elevation Model (DEM), from which most of the causal factors, including internal relief, slope, aspect, classified landforms, classified curvature, and topographic wetness index (TWI), were derived. This highlights the critical role of the DEM in landslide susceptibility assessment. Application of Multi-Factor Approaches : The research involved the application of both eight-factor and ten-factor based approaches within a semi-quantitative framework. Innovation in Landslide Susceptibility Maps : A groundbreaking technique for synthetic classification of the generated landslide susceptibility maps within the semi-quantitative model was implemented. Future evaluation of the TWI variable : Future studies are intended to comprehensively evaluate the Topographic Wetness Index (TWI) at various pixel resolutions and scales to determine whether a TWI value of 12 is a viable threshold for assessing landslide susceptibility. Applicability of Study Methodology : The study's comprehensive methodology demonstrates the potential for transferability to landslide susceptibility mapping over large geographic regions, including countries or continents. Impact of DEM resolution : The increase in DEM resolution plays a critical role, potentially revealing hidden relationships or rules within coarse-resolution DEMs. Strategic partitioning for area analysis : For extensive landslide susceptibility analyses, strategic subdivision of the study area into smaller sub-basin boundaries is recommended. This approach allows for a discrete and more accurate analysis of each basin and promotes a deeper understanding of the unique basin characteristics that influence landslides. Expanded Subdivision for Advanced Study : To facilitate more detailed analysis in the future, it is suggested that the study basins be subdivided into sub-basins. This, combined with finer pixel resolution and larger scales, will result in a more accurate and comprehensive assessment of landslide susceptibility. This study presents the first comprehensive assessment of landslide susceptibility for Türkiye, analyzing various factors such as slope, topography, and land cover across all major drainage basins. The research provided valuable insights for future susceptibility assessments. Successful implementation of such detailed studies requires robust interagency coordination. This coordination facilitates the strategic allocation of qualified personnel, time, financial resources, and reliable data sets, thereby fostering significant progress in the field of landslide hazard assessment. Declarations Authors’ contributions KO and HA: conceived the idea for the manuscript. KO: collected datasets, analyzed, compiled the GIS maps, and drafted the manuscript. HA: provided supervision, verification, editing, and modification. KO and HA: collaborated in finalizing the manuscript. Funding This research was supported by the Middle East Technical University (METU) Research Fund Project No. BAP-03-09-2010-01. Availability of data and material Digital elevation model was sourced from the CGIAR-CSI website (http://srtm.csi.cgiar.org), the CORINE Land Cover 2018 seamless vector data was sourced from the CLC website (https://land.copernicus.eu/pan-european/corine-land-cover/clc2018), the 1:500,000 scale hard-copy geological maps and landslide inventory maps were purchased from the Turkish Mineral Research and Exploration General Directorate, the rainfall records, the rainfall records were compiled from the archives of the Turkish State Meteorological Service, the earthquake data layer was digitized from the Earthquake Map of Türkiye published by the Disaster and Emergency Management Authority, landslide statistics were compiled from the Disaster and Emergency Management Authority (https://www.afad.gov.tr/kurumlar/afad.gov.tr/35429/xfiles/Turkiye_de_Afetler.pdf), and GoogleEarth software (https://earth.google.com). Code availability Not applicable. Conflicts of interest We declare that we do not have any commercial or associative interest that represents a conflict of interest or competing interest in connection with the work submitted. Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. References Abella EAC, van Westen CJ (2007) Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation. Landslides, 4:311–325. https://doi.org/10.1007/s10346-007-0087-y Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101-111. https://doi.org/10.1016/j.geomorph.2018.06.006 AFAD (2018) Disaster Management and Natural Disaster Statistics in Turkey. In: Disaster and Emergency Management Authority. https://www.afad.gov.tr/kurumlar/afad.gov.tr/35429/xfiles/Turkiye_de_Afetler.pdf. 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Engineering Geology, 71(3-4), pp.289-302 Malczewski J (1999) GIS and Multicriteria Decision Analysis. New York: John Wiley & Sons Monod O, Kozlu H, Ghienne JF, Dean WT, Günay Y, Hérissé AL, Paris F, Robardet M (2003) Late Ordovician glaciation in southern Turkey. Terra Nova 15(4):249–257. https://doi.org/10.1046/j.1365-3121.2003.00495.x Nilsen TH, Wright RH, Vlasic C, Spangle WE (1979) Relative slope stability and land-use planning in the San Francisco Bay region, California. US Geological Survey Professional Paper, 944, 104 pp Okalp K (2013) Landslide susceptibility assessment of Turkey by using qualitative and semi-quantitative methods, Ph.D. Dissertation, Middle East Technical University. http://etd.lib.metu.edu.tr/upload/12616612/index.pdf. Accessed 18 April 2020 Okalp K, Akgün H (2016) National level landslide susceptibility assessment of Turkey utilizing public domain dataset. Environmental Earth Sciences, 75, 847. https://doi.org/10.1007/s12665-016-5640-3 Okalp K, Akgün H (2022) Landslide susceptibility assessment in medium-scale: case studies from the major drainage basins of Turkey. Environmental Earth Sciences, 81(8), 244. https://doi.org/10.1007/s12665-022-10355-3 Okay AI (2008) Geology of Turkey: a synopsis. Anschnitt 21:19–42 Reis S, Yalçın A, Atasoy M, Nisanci R, Bayrak T, Erduran M, Sancar C, Ekercin S (2012) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey). Environmental Earth Sciences, 66(7): 2063–2073. https://doi.org/10.1007/s12665-011-1432-y Saaty TL (1977) A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15:234–281. https://doi.org/10.1016/0022-2496(77)90033-5 Saaty TL (1980) The Analytic Hierarchy Process, McGraw Hill, New York. Reprinted by RWS Publications, 4922 Ellsworth Avenue, Pittsburgh, PA, 15213, 2000 Saaty TL (2000) Fundamentals of decision making and priority theory with the analytic hierarchy process, RWS Publications, Pittsburg, USA Saaty TL, Vargas LG (2012) How to make a decision. In: Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. International Series in Operations Research & Management Science, vol 175. Springer, Boston, MA, pp. 1-21. https://doi.org/10.1007/978-1-4614-3597-6 Sabatakakis N, Koukis G, Vassiliades E, Lainas S (2013) Landslide susceptibility zonation in Greece. Natural Hazards, 65: 523–543. https://doi.org/10.1007/s11069-012-0381-4 Soeters R, van Westen CJ (1996) Slope instability recognition, analysis, and zonation. In: Turner, A.K., Schuster, R.L., (eds) Landslides, investigation and mitigation, vol 247, Transportation Research Board, National Research Council, Special Report. National Academy Press, Washington, D.C., pp 129–177 Şengör AMC, Yilmaz Y (1981) Tethyan evolution of Turkey: a plate tectonic approach. Tectonophysics 75:181–241. https://doi.org/10.1016/0040-1951(81)90275-4 Şensoy S, Demircan M, Ulupınar Y, Balta İ (2013) Climate of Turkey, Turkish State Meteorological Service. http://www.mgm.gov.tr/files/en-US/climateofturkey.pdf. Accessed 6 May 2013 TUIK (2012) Turkish Statistical Institute. http://www.turkstat.gov.tr. Accessed 3 Feb 2013 van Westen CJ (1997) Statistical landslide hazard analysis. ILWIS 2.1 for Windows Applications Guide, ITC Publication, Enschede, pp. 73–84 Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena, 72: 1–12. https://doi.org/10.1016/j.catena.2007.01.003 Yoshimatsu H, Abe S (2006) A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method. Landslides, 3, 149–158. https://doi.org/10.1007/s10346-005-0031-y Additional Declarations No competing interests reported. Supplementary Files KOENGE3OnlineResource1.pdf KOENGE3OnlineResource2.pdf KOENGE3OnlineResource3.pdf KOENGE3OnlineResource4.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4704929","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328270393,"identity":"0b109e8b-65f0-46ca-a36a-fb4b6206ea87","order_by":0,"name":"Kıvanç Okalp","email":"","orcid":"","institution":"Middle East Technical University (METU)","correspondingAuthor":false,"prefix":"","firstName":"Kıvanç","middleName":"","lastName":"Okalp","suffix":""},{"id":328270394,"identity":"7a8b712b-7d62-4e00-8e1f-d546b867cb99","order_by":1,"name":"Haluk Akgün","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACAwY2EGUD5bIRryUNqhpI8BCp5TAJWszZjyW/5t1xPppfvseA4UPZYQZ76QP4tVj2pB2z5j1zO3dmG48B44xzhxl4+BIIOOxAepsxb9vt3A3HeAyYeduAWgi5zOD8c5CWc7n7QVr+EqXlRtrhx7xtB3I3sAG1MBKn5Vka49y25NwZx9IKDvacS+fhOUPQYWnGH9622eX2Nx/e+OBHmbUcew8BLUDAJgFjHWAgIlpAgPkDMapGwSgYBaNgBAMAquA/5VPJaKMAAAAASUVORK5CYII=","orcid":"","institution":"Middle East Technical University (METU)","correspondingAuthor":true,"prefix":"","firstName":"Haluk","middleName":"","lastName":"Akgün","suffix":""}],"badges":[],"createdAt":"2024-07-08 10:45:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4704929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4704929/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61600230,"identity":"62adfd35-36dc-4896-9285-513dc99f7952","added_by":"auto","created_at":"2024-08-01 17:36:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127092,"visible":true,"origin":"","legend":"\u003cp\u003eThe boundaries of the major drainage basins in Türkiye (Okalp 2013)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4704929/v1/8d133d556eabe43d4a233a6e.png"},{"id":61600223,"identity":"e275f583-b542-4f39-80a9-cc61ef8392c6","added_by":"auto","created_at":"2024-08-01 17:36:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":298160,"visible":true,"origin":"","legend":"\u003cp\u003eUnclassified landslide susceptibility maps for the Western Mediterranean Basins; \u003cstrong\u003e(a)\u003c/strong\u003ewith eight factors, \u003cstrong\u003e(b)\u003c/strong\u003e with ten factors (please refer to the details of the remaining 25 drainage basins in the Supplementary Material Section as Online Resource 3)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4704929/v1/35180765b13ca2102f094dba.png"},{"id":61600224,"identity":"48a75182-a4c9-465e-babc-d80c4954806b","added_by":"auto","created_at":"2024-08-01 17:36:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":591988,"visible":true,"origin":"","legend":"\u003cp\u003eDetermining landslide susceptibility classes for the Western Mediterranean Basin; \u003cstrong\u003e(a)\u003c/strong\u003e Histogram curves for the inside and outside of landslide boundaries, \u003cstrong\u003e(b)\u003c/strong\u003e Receiver Operating Characteristic (ROC) curve analysis, and \u003cstrong\u003e(c)\u003c/strong\u003e Synthetically classified landslide susceptibility map (please refer to the details of the remaining 25 drainage basins in the Supplementary Material Section as Online Resource 4)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4704929/v1/e494017d92daf88cabc36993.png"},{"id":61600221,"identity":"9c2183a9-cd6b-4e34-8abb-1e92ed9aa8bd","added_by":"auto","created_at":"2024-08-01 17:36:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":604085,"visible":true,"origin":"","legend":"\u003cp\u003eLandslide susceptibility map of Türkiye\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4704929/v1/5cd03e1c4ccacf43d9997428.png"},{"id":62505419,"identity":"a4c4a582-6195-4825-86b8-9bf2c44497dd","added_by":"auto","created_at":"2024-08-15 04:17:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3188959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4704929/v1/1f667bb4-2876-4c14-a316-d5aa7b6facf3.pdf"},{"id":61600216,"identity":"f81485f8-c836-4a60-b9db-6b12bbcc6acf","added_by":"auto","created_at":"2024-08-01 17:36:10","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":718410,"visible":true,"origin":"","legend":"","description":"","filename":"KOENGE3OnlineResource1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4704929/v1/1842b3a6eacdaa38ca44eaba.pdf"},{"id":61600222,"identity":"20ec17b8-8d55-4052-a3c8-2971b4d3c2fc","added_by":"auto","created_at":"2024-08-01 17:36:13","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":3742439,"visible":true,"origin":"","legend":"","description":"","filename":"KOENGE3OnlineResource2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4704929/v1/f9beb88a0b6e074e0563e1c1.pdf"},{"id":61600236,"identity":"ef9cf472-c317-4eb9-b66b-b500182e312e","added_by":"auto","created_at":"2024-08-01 17:36:21","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":10264779,"visible":true,"origin":"","legend":"","description":"","filename":"KOENGE3OnlineResource3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4704929/v1/ef856c92bf557c78883b23aa.pdf"},{"id":61600229,"identity":"a8cf123b-1c02-4d29-995f-53ffc3b58336","added_by":"auto","created_at":"2024-08-01 17:36:17","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":8317665,"visible":true,"origin":"","legend":"","description":"","filename":"KOENGE3OnlineResource4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4704929/v1/bc911f69cb9f7b72bb48b6be.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Landslide Susceptibility of Türkiye at Drainage Basin Scale: A Semi-quantitative Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVulnerable rural and mountainous regions are disproportionately affected by landslides, a major geohazard that causes significant human and economic costs worldwide. The increasing impacts of climate change, including increased precipitation and extreme weather events, are exacerbating the frequency and severity of landslides, particularly in T\u0026uuml;rkiye's extensive mountainous regions (Okalp and Akg\u0026uuml;n \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Most landslides in T\u0026uuml;rkiye, which often coincide with floods, are associated with heavy rainfall events, which are further complicated by climate change-related weather anomalies. The Center for Research on the Epidemiology of Disasters (CRED) reports that landslides are responsible for at least 17% of natural disaster casualties worldwide, a proportion that is expected to increase as the effects of climate change intensify (Lacasse et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the mid-20th century, the Disaster and Emergency Management Authority (AFAD) in T\u0026uuml;rkiye has been methodically documenting landslide damage. These records show that there have been 23,041 landslides since 1950, causing over 700 deaths and affecting 5,472 communities, with the risk increasingly exacerbated by climate change (G\u0026ouml;k\u0026ccedil;e et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; AFAD \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fidan \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In response to this threat, AFAD has supported the relocation of 59,345 people to safer areas between 1950 and 2008 (G\u0026ouml;k\u0026ccedil;e et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough numerous studies have outlined various methods for assessing landslide hazard, there remains a significant gap in the thorough assessment of landslide susceptibility over large geographic areas, such as countries in their entirety (Guzzetti \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Yoshimatsu and Abe \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Abella and van Westen \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sabatakakis et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gaprindashvili and van Westen \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This gap highlights the critical need to broaden research perspectives from local to national and continental scales, which would increase the robustness and relevance of landslide susceptibility indices.\u003c/p\u003e \u003cp\u003eIn the context of T\u0026uuml;rkiye, localized studies, such as those conducted in Azdavay, Kastamonu, and Rize, have provided invaluable insights and methodological contributions to the field (Ercanoglu and Temiz \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Reis et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, the need for comprehensive, nationwide assessments remains paramount for a holistic understanding of landslide susceptibility across T\u0026uuml;rkiye, which is critical for the development of effective landslide mitigation and management strategies.\u003c/p\u003e \u003cp\u003eThe development of a landslide susceptibility map for T\u0026uuml;rkiye using a qualitative approach by Okalp and Akg\u0026uuml;n (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) represents a significant advance in understanding the country's geologic hazards. This research has extended previous efforts by using the Analytical Hierarchy Process (AHP) to meticulously assess landslide susceptibility and identify vulnerable regions across T\u0026uuml;rkiye, thereby providing a comprehensive analysis for the assessment of landslide risks in the country (Okalp \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It is believed that the study's comprehensive and detailed approach significantly expanded the field of landslide susceptibility assessment and provided a reliable and effective framework for future research and assessment efforts, which are critical in ensuring community safety and resilience in the frame of climate change challenges.\u003c/p\u003e \u003cp\u003eThis research utilizes a GIS-based, semi-quantitative approach, known as the Analytical Hierarchy Process (AHP), to evaluate landslide susceptibility within major drainage basins across T\u0026uuml;rkiye. The AHP was selected for its ability to integrate multiple parameters, facilitating the production of robust landslide susceptibility maps. Publicly accessible, medium-scale datasets were utilized for the regional analysis. After rigorous validation, an innovative synthetic classification procedure was developed to delineate susceptibility zones. This research attempts to represent a significant advance in the assessment of landslide susceptibility for all major Turkish catchments. The described method offers decision-makers a useful framework for assessing landslide susceptibility, hazard, and risk at regional scales, with potential applications at national or continental levels.\u003c/p\u003e"},{"header":"Study region","content":"\u003cp\u003eOccupying a strategic position at the crossroads of Europe and Asia, T\u0026uuml;rkiye's 783,562 km\u0026sup2; landmass encompasses the Anatolian Peninsula and eastern Thrace. The Black Sea, Mediterranean Sea, Aegean Sea, and Marmara Sea form an extensive 7,200 km coastline (CIA World Factbook 2002).\u003c/p\u003e \u003cp\u003eAnatolia, a vast semi-arid plateau surrounded by coastal mountains, makes up about 97% of T\u0026uuml;rkiye's territory. Eastern Thrace, the remaining 3% of the landmass, is separated by the Bosphorus, the Dardanelles, and the Sea of Marmara. This European part is home to over 10% of T\u0026uuml;rkiye's population (TUIK \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Towering over the eastern border is Mt. Ararat, the country's highest peak at 5,137 meters.\u003c/p\u003e \u003cp\u003eA recent surge in geological data collection has illuminated T\u0026uuml;rkiye's complex geological history. This complexity is prominently reflected in the diverse geological features found throughout the Anatolian Peninsula. In particular, the region, which is bordered by several seas, displays formations that span a wide range of time periods. The Black Sea, is an ancient oceanic backarc basin that has originated in the Cretaceous period. In contrast, the younger Aegean Sea began its development during the Oligo-Miocene epoch (Garfunkel \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eT\u0026uuml;rkiye, which ranks tenth in the world in terms of seismic activity, experiences a complex tectonic environment driven by the Aegean Arc and the North Anatolian Fault Zone (Bayrak et al., 2008). The geology of the Black Sea is categorized into three distinct tectonic divisions: the Pontides, the Anatolides-Taurides, and the Arabian Platform, each characterized by its own geological narrative (Okay \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Pontides area, situated to the north of the İzmir-Ankara-Erzincan suture, displays the impact of the Alpide orogeny, a mountain-forming event, as seen in its folded and thrust-faulted structures. In contrast, the Anatolide-Tauride terrane in southern T\u0026uuml;rkiye is marked by extensive deformation and regional metamorphism, also due to Alpide orogenic activity (Monod et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The region of Southeast Anatolia, which extends from the Arabian platform, was previously separated from the Anatolide-Tauride by the southern branch of the Neo-Tethys ocean, a division still evident in the Assyrian suture (Şeng\u0026ouml;r and Yilmaz \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). During the Oligo-Miocene epoch, a tectonic consolidation occurred as these terranes merged. This era was characterized by continental sedimentation, calcalkaline magmatism, extensional forces, and strike-slip faulting, all contributing to the formation of T\u0026uuml;rkiye's present geological features and active structures (Okay \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eT\u0026uuml;rkiye has a mosaic of climatic zones due to its diverse geography. The Aegean and Mediterranean coasts experience a classic Mediterranean climate characterized by hot, dry summers and mild, wet winters. In contrast, the Black Sea coast receives significantly more precipitation, especially in the east (up to 2200 mm annually) (Şensoy et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), resulting in a temperate oceanic climate. The Sea of Marmara features a transitional climate, with occasional snowfall during winter. Encircled by mountains, Central Anatolia is subject to a continental climate, characterized by severe winters. In the eastern regions, temperatures can vary between \u0026minus;\u0026thinsp;30\u0026deg;C and \u0026minus;\u0026thinsp;38\u0026deg;C, whereas in the western areas, the average winter temperature remains below 1\u0026deg;C. Summers in Central Anatolia are typically hot and dry, with temperatures frequently rising above 30\u0026deg;C. The national average annual precipitation is approximately 640 mm, exhibiting a slightly decreasing trend (Şensoy et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The Konya and Malatya plains represent the driest areas, receiving less than 300 mm of rainfall each year.\u003c/p\u003e \u003cp\u003eLandslide susceptibility mapping has become more sophisticated over time. Early methods have simply overlain geologic and topographic features on existing landslide locations to identify areas at risk. Today, researchers use advanced techniques such as statistical analysis (bivariate and multivariate), logistic regression, the Analytical Hierarchy Process (AHP), fuzzy logic, and even artificial neural networks.\u003c/p\u003e \u003cp\u003eThis study has utilized AHP due to its ability to minimize subjectivity and ensure consistent judgments in the assessment of landslide susceptibility. Hydrological analyses were employed to assess landslide susceptibility in major Turkish drainage basins, each distinguished by its unique climatic, landform, and seismic features. The Analytical Hierarchy Process (AHP) with a spatial resolution of 90 meters and an intermediate scale of 1:500,000, incorporating ten causal factors was performed. Following the analysis, the two least influential factors were excluded for re-evaluation, ensuring a comprehensive and reliable susceptibility assessment for each basin (Okalp \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAlthough landslide susceptibility assessment has received considerable attention over the last two decades, consensus on a standardized methodology remains elusive (Ercanoglu and Gokceoglu \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). A variety of qualitative and quantitative approaches can be used to produce landslide susceptibility maps (Soeters and van Westen \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Qualitative methods, such as those based on landslide inventories and heuristic analysis, often incorporate subjective expert knowledge and experience. While some methods introduce semi-quantitative aspects through ranking and weighting of observed landslide events, subjectivity remains a core component. Conversely, quantitative methods, including statistical and deterministic techniques, strive for greater objectivity by emphasizing data-driven analysis rather than expert opinion (Soeters and van Westen \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLandslide susceptibility mapping relies on several approaches, each with different characteristics. Landslide inventory, a basic technique, uses the spatial distribution of landslide deposits to directly generate susceptibility maps. Heuristic analysis uses geomorphic analysis and qualitative map combination, relying on expert judgment and thematic map weighting. Statistical approaches, including bivariate and multivariate analysis, integrate factor maps with landslide distribution data. Weighting values are assigned based on the spatial correlation and the occurrence or non-occurrence of landslides within specified land units. Deterministic methods, which necessitate comprehensive geotechnical and hydrological data, are well-suited for large-scale analyses in regions with uniform geomorphic and geological conditions.\u003c/p\u003e \u003cp\u003eThe selection of an appropriate method for assessing landslide susceptibility depends on the scale of the analysis (Soeters and van Westen \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In the context of national-level assessments, where susceptibility levels may not warrant the extensive time investment required by highly detailed methods, a semi-quantitative approach offers a valuable compromise. This study uses a semi-quantitative approach implemented within the ArcGIS Spatial Analyst extension. This method involves organizing relevant criteria into a \u0026ldquo;criteria tree\u0026rdquo; structure. Each criterion is then standardized, weighted, and subsequently integrated to produce one or more \u0026ldquo;composite index maps\u0026rdquo;. Notably, this approach is based on Saaty\u0026rsquo;s (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) Analytical Hierarchy Process (AHP). Recent applications of AHP in landslide susceptibility assessment have underscored its effectiveness in decision-making scenarios (Saaty and Vargas \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLandslide susceptibility assessment using the AHP\u003c/h2\u003e \u003cp\u003eThe Analytical Hierarchy Process (AHP), developed by Saaty (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), provides a powerful quantitative framework for multi-criteria decision making. A flexible and comprehensive tool for the analysis of complex problems, AHP facilitates the structuring of complexity and systematic judgment, making it particularly well suited to scenarios involving multiple, often conflicting, criteria. AHP is characterized by its method of deriving ratio scale priorities or weights rather than arbitrary assignments (Yalcin \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This is achieved by constructing a hierarchical problem structure and making pairwise relative comparisons. This approach effectively merges objective and subjective elements into the decision-making process, offering a solid and transparent framework.\u003c/p\u003e \u003cp\u003eGrounded in the principles of decomposition, comparative judgment, and priority synthesis (Malczewski \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), the Analytical Hierarchy Process (AHP) uses a structured approach to decision making. This includes hierarchical problem organization, pairwise comparisons, incorporation of redundant judgments for consistency, the eigenvector method for weight derivation, and thorough consistency analysis. The effectiveness of AHP in landslide susceptibility assessment is well established, with a substantial body of research demonstrating its successful application (Chung and Leclerc \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Barredo et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ayalew and Yamagishi \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Komac \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Akg\u0026uuml;n and Bulut \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Yalcin \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study employs the Analytical Hierarchy Process (AHP) to systematically assign preference scores to various factors influencing landslide susceptibility. The approach follows Saaty's (2000) proposal, which uses a numerical relational scale to facilitate pairwise comparisons between attributes (i.e., layer classes or parameters). A key strength of AHP is its ability to assess the internal consistency of these pairwise assessments. Saaty (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) demonstrated that a consistent reciprocal matrix - where comparisons are transitive - yields a maximum eigenvalue (λ\u003csub\u003emax\u003c/sub\u003e) equal to the number of comparisons (n). Based on this concept, a consistency index (CI) is calculated and a consistency ratio (CR) is derived (Saaty \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). CR values less than or equal to 10% indicate acceptable consistency. Judgments above this threshold require revision to ensure robust and reliable weightings within the AHP framework.\u003c/p\u003e \u003cp\u003eThe Landslide Susceptibility Index (LSI) is calculated utilizing weights and scores derived from the eigenvalues of AHP matrices. These matrices elucidate the relationships between various influencing factors and their respective classes. After obtaining LSI values, categorization into different landslide susceptibility classes provides the final landslide susceptibility zonation map (Okalp \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Okalp and Akg\u0026uuml;n \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFactor selection\u003c/h2\u003e \u003cp\u003eWhile various landslide susceptibility studies include a wide range of factors - both natural (i.e., lithology, lineament) and artificial (i.e., roads), causal (i.e., slope, lithology) and triggering (i.e., rainfall, seismicity) - this study adopts a focused approach for greater clarity and efficiency. Although it is possible to include numerous factors, as demonstrated in discriminant models (Guzzetti et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), a focused approach can offer advantages in analysis and interpretation. In this study, ten key factors were carefully selected, covering both causal elements (i.e., slope, internal relief and lithology) and triggering mechanisms (i.e., rainfall intensity and earthquake). This targeted selection aimed to provide a comprehensive, yet manageable assessment of the drivers of landslide susceptibility.\u003c/p\u003e \u003cp\u003eThe selection of factors in this study prioritized both data accessibility and established importance in landslide hazard research. A resolution of 90 m x 90 m was considered appropriate for the assessment of landslide hazard in T\u0026uuml;rkiye at a scale of 1:500,000 using the publicly available CSI-SRTM DEM version 4.1 published at the CGIAR-CSI website (Jarvis et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The importance of a high-quality Digital Elevation Model (DEM) must be emphasized, as it is the basis for deriving most of the factors investigated (Okalp \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Calculations for slope angle and internal relief were performed using methods proposed by Hickey (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) within the Spatial Analyst extension of ArcGIS. A 11 x 11 pixel window was used for the SRTM DEM used. Additional DEM derivatives were calculated within the SAGA GIS environment. The aspect layer, which identifies the direction of slope, was generated at a resolution of 90 meters. The Topographic Wetness Index (TWI), a causal factor used to analyze spatial scale effects on hydrologic processes, was calculated based on local upslope area and local slope (Beven and Kirkby 1979). The Topographic Position Index (TPI), which compares the elevation of a cell to the average elevation of the surrounding area, was calculated using SAGA GIS, producing a layer with a resolution of 90 meters. Similarly, profile and plan curvature, which influence water flow dynamics, were analyzed in SAGA GIS and included as causal factors at 90 m resolution (Okalp \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe land use map was derived from the seamless vector data provided by CORINE Land Cover 2018 (CLC \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This robust dataset, harmonized for the 2018 reference year, used high-resolution imagery from the Landsat ETM\u0026thinsp;+\u0026thinsp;and Sentinel-2 satellites. The comprehensive hierarchical classification scheme included 44 land cover subclasses, structured into three levels. To optimize the dataset for landslide susceptibility assessment, a synthetic classification procedure was implemented that integrated the original CORINE Level 3 subclasses (Okalp \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLithological data for this study were sourced from geological maps at a scale of 1:500,000, published by the General Directorate of Mineral Research and Exploration (MTA) of T\u0026uuml;rkiye. After digitization, these lithological units were reclassified into a more manageable set of 24 synthetic classes as described in Okalp (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Earthquake data layers were integrated through detailed digitization of the 2018 T\u0026uuml;rkiye Earthquake Map produced by the Disaster and Emergency Management Authority (AFAD). This map represents the latest government-approved knowledge of seismic activity in the country. The map classifies Peak Ground Acceleration (PGA) with a 10% probability of being exceeded in 50 years into five distinct zones delineated by specific thresholds (0.10 g/0.167 g/0.33 g/ 0.50 g).\u003c/p\u003e \u003cp\u003eRainfall data, essential for landslide susceptibility analysis, were derived from meteorological station records spanning a robust 65-year period. Monthly total rainfall measurements were aggregated to produce annual mean total rainfall values. The inverse distance weighting (IDW) method was used to spatially interpolate these values which resulted in a continuous rainfall map with a fine-grained resolution of 90 m pixels, suitable for detailed analysis within the study area (Okalp \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLandslide susceptibility mapping has advanced considerably from the initial qualitative methods, which involved overlaying geological and morphological slope characteristics onto landslide inventories (Nielsen et al. 1979). Contemporary assessments employ sophisticated quantitative techniques such as AHP, bivariate analysis, and others (Carrara \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; van Westen \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Dai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lee and Min \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ercanoglu and Gokceoglu \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Komac \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The Analytical Hierarchy Process (AHP) was used in this research owing to its ability to synthesize various parameters. The strength of AHP lies in its ability to establish correlations between factors within a structured framework, which facilitates the generation of more reliable and consistent landslide susceptibility maps.\u003c/p\u003e \u003cp\u003eThe management and analysis of high-resolution geospatial data for nationwide landslide susceptibility assessment poses a significant computational challenge. To achieve a high-resolution landslide susceptibility map of T\u0026uuml;rkiye, a reduction in dataset file sizes was crucial. This study adopted a segmentation approach that divided T\u0026uuml;rkiye into meaningful geomorphologic zones, specifically drainage basins. Drainage basins serve as natural topographic units defined by a network of tributaries converging on a main outlet and bounded by ridge lines. This segmentation strategy allowed for more efficient data processing and facilitated subsequent landslide susceptibility analysis.\u003c/p\u003e \u003cp\u003eIn order to optimize the analysis and achieve detailed spatial results, T\u0026uuml;rkiye was segmented into its 26 major drainage basins. Each basin was individually modeled with a fine spatial resolution of 90 meters and an appropriate scale of 1:500,000 for assessing landslide susceptibility. Initially, the Analytical Hierarchy Process (AHP) was applied to each basin, incorporating a set of ten influential factors. Rigorous post-analysis evaluation identified the two least influential factors - aspect and rainfall intensity. These were subsequently excluded and each basin was recalculated using an optimized set of eight factors. The systematic use of AHP with both the initial and refined factor sets produced results for each basin, including comparative graphs and Receiver Operator Characteristic (ROC) curves. Finally, these zonal results were integrated to produce a comprehensive and highly detailed landslide susceptibility map of T\u0026uuml;rkiye. Visual representations of the major basin boundaries are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and historical Landslide Area Ratios (LAR) of the basins are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHistorical Landslide Area Ratios (LAR) of basins (Okalp \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasin Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandslide Area (A; km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBasin Area (B; km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLAR\u003c/p\u003e \u003cp\u003e(A/B; %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Maritsa-Ergene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14464.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e436.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23113.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusurluk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24293.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth Aegean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9952.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGediz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16976.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKucuk Menderes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7029.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuyuk Menderes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26010.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern Mediterranean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e299.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21084.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntalya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20213.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurdur Endorheic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6273.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAkarcay Endorheic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7954.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSakarya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1077.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63256.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern Black Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3060.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28967.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYesilirmak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1734.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39614.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKizilirmak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1652.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82100.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKonya Endorheic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49805.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern Mediterranean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e484.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21657.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeyhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22135.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Asi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7856.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCeyhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21487.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper Euphrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6058.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121677.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern Black Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e665.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22852.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper Coruh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1014.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20251.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper Aras\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1276.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28099.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLake Van Endorheic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e373.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17916.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper Tigris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1304.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54278.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn this section, a meticulous analysis has been performed for each study region based on its drainage basin delineation, with individual results presented for greater clarity and understanding. Subsequent subsections provide a comprehensive overview of the validation process for the formulated landslide susceptibility maps, using ROC curves to ensure increased accuracy and reliability. A thorough explanation of the selection methodology is provided, including both eight- and ten-factor-based approaches to ensure a robust and systematic analysis. An explanation of the synthetic classification process is also presented. This process is critical to the optimal selection and categorization of maps, providing a solid foundation for further discussion and evaluation, and reinforcing the comprehensive and methodological approach of this study.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eDrainage basin metrics for historical landslides\u003c/h2\u003e\n \u003cp\u003eA detailed comparative analysis of the historical landslide metrics has been carried out to identify both unique and common characteristics that contribute to landslide susceptibility in the different basins of T\u0026uuml;rkiye. This comprehensive analysis has highlighted not only the significant variations in geomorphic and hydrologic parameters that have historically influenced landslide occurrence, but also the constants that tie these diverse regions together.\u003c/p\u003e\n \u003cp\u003eIn particular, the seismic zones, especially zones 1 and 2, show a significant correlation with landslide incidence, highlighting the influence of seismic activity on slope destabilization. The morphological typology of slope curvature and landform classes across basins accentuates the susceptibility of specific features, with planar slopes and open landforms consistently identified as landscapes of increased susceptibility. A common pattern is observed in the slope angles associated with landslides, predominantly between 5\u0026deg; and 10\u0026deg;, although basins such as Buyuk Menderes and Western Mediterranean reflect a slight increase in this range from 10\u0026deg; to 15\u0026deg;.\u003c/p\u003e\n \u003cp\u003eThe convergence of human activities and the natural environment is captured by the land cover analysis, which shows that agricultural land and forest areas affected by land use change are often involved in landslide events. This combination highlights the critical need for careful land management practices in landslide-prone regions and demonstrates the complex relationship between anthropogenic factors and natural susceptibility.\u003c/p\u003e\n \u003cp\u003eThe integration of geomorphologic findings with climatologic data reveals a robust correlation between precipitation distribution and landslide occurrence. The majority of landslide events occur within well-defined rainfall ranges. This finding is supported by the uniformity of the Topographic Wetness Index (TWI) values, which predominantly range from 12 to 13. Likewise, the internal relief, which varies from 50 m/km\u0026sup2; to 200 m/km\u0026sup2;, shows a significant relation with the occurrence of landslides. These results underscore the predictive power of hydrological and topographic characteristics in identifying areas of increased landslide susceptibility.\u003c/p\u003e\n \u003cp\u003eA review of the metrics across basins shows remarkable resemblances. For example, the Lower Maritsa-Ergene and Marmara basins, despite their different elevations and activity levels, have closely related rainfall and TWI values, suggesting similar hydrological influences. In the same way, the North Aegean and Susurluk basins, with their similar elevation ranges and internal relief values, show the presence of similar topographic features that may affect landslide susceptibility.\u003c/p\u003e\n \u003cp\u003eIf this analysis is to be extended, other basins such as the Western Mediterranean, Antalya and Seyhan show moderate landslide activity influenced by a combination of geological formations and anthropogenic land cover types. The Eastern Black Sea and Upper Tigris basins stand out for their high landslide activity, characterized by wide elevation ranges and significant annual precipitation, highlighting the strong influence of climatic conditions. The Eastern Mediterranean and Upper Euphrates Basins, with moderate activity and particular lithological compositions, contribute to the complex landscape of landslide activity.\u003c/p\u003e\n \u003cp\u003eIn sharp contrast, the Lower Asi and Konya Endorheic Basins exhibit remarkably low landslide activity. This observation highlights the critical role of land management practices in maintaining slope stability, particularly in agricultural and semi-natural landscapes.\u003c/p\u003e\n \u003cp\u003eGrouping basins according to common characteristics provides invaluable insights for better risk management. The North Aegean, Gediz, and Lower Maritsa-Ergene basins share very low to low landslide activity and landslide-prone geological formations, mainly continental clastic rocks, forming a common susceptibility profile. Conversely, the Marmara Basin, which has some similar lithological features, reflects moderate activity, suggesting the role of location and various factors such as rainfall in affecting landslide activities.\u003c/p\u003e\n \u003cp\u003eThese similarities and contrasts between basins emphasize a strategic perspective for landslide risk management, pointing towards regions where similar mitigation strategies may be effective. It also provides a framework for prioritizing research and interventions based on common risk profiles to increase the effectiveness of landslide mitigation.\u003c/p\u003e\n \u003cp\u003eThe following comprehensive Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e serves as an essential reference for a deeper, basin-by-basin discussion that will explore these dynamics, aid in identifying targeted intervention points for each basin, and enrich the collective understanding of the multifaceted nature of landslide susceptibility in T\u0026uuml;rkiye.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMajor Drainage Basin Characteristics for Historical Landslides in T\u0026uuml;rkiye\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBasin Name (No)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElevation Range (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLandslide Activity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLithologies Prone to Landslides\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRainfall Range (mm) (in general)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTWI Values Range (in general)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInternal Relief Range (m/km\u0026sup2;) (in general)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower Maritsa-Ergene (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNotably low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks, continental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e496\u0026ndash;798\u003c/p\u003e\n \u003cp\u003e(600\u0026ndash;700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;22\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;355\u003c/p\u003e\n \u003cp\u003e(50\u0026ndash;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarmara (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks, clastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e533\u0026ndash;1263\u003c/p\u003e\n \u003cp\u003e(600\u0026ndash;800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;700\u003c/p\u003e\n \u003cp\u003e(50\u0026ndash;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSusurluk (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;2529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e435\u0026ndash;834\u003c/p\u003e\n \u003cp\u003e(600\u0026ndash;700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;806\u003c/p\u003e\n \u003cp\u003e(100\u0026ndash;200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Aegean (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndifferentiated volcanic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e518\u0026ndash;946\u003c/p\u003e\n \u003cp\u003e(600\u0026ndash;700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;22\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;710\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGediz (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;2298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e446\u0026ndash;1044\u003c/p\u003e\n \u003cp\u003e(400\u0026ndash;500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;856\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKucuk Menderes (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;2134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e483\u0026ndash;926\u003c/p\u003e\n \u003cp\u003e(700\u0026ndash;800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;804\u003c/p\u003e\n \u003cp\u003e(100\u0026ndash;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuyuk Menderes (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;2519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e421\u0026ndash;1213\u003c/p\u003e\n \u003cp\u003e(500\u0026ndash;700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;827\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Mediterranean (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;3039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks, continental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e420\u0026ndash;1317\u003c/p\u003e\n \u003cp\u003e(500\u0026ndash;700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;22\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1044\u003c/p\u003e\n \u003cp\u003e(200\u0026ndash;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntalya (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;2972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u0026ndash;763\u003c/p\u003e\n \u003cp\u003e(700\u0026ndash;800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1255\u003c/p\u003e\n \u003cp\u003e(200\u0026ndash;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurdur Endorheic (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e821\u0026ndash;2738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks, ophiolitic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e488\u0026ndash;887\u003c/p\u003e\n \u003cp\u003e(500\u0026ndash;600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;901\u003c/p\u003e\n \u003cp\u003e(200\u0026ndash;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAkarcay Endorheic (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e949\u0026ndash;2576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e314\u0026ndash;621\u003c/p\u003e\n \u003cp\u003e(500\u0026ndash;600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;749\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSakarya (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;2460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286\u0026ndash;1319\u003c/p\u003e\n \u003cp\u003e(300\u0026ndash;400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;837\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Black Sea (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;2397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e382\u0026ndash;1275\u003c/p\u003e\n \u003cp\u003e(800\u0026ndash;1000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;970\u003c/p\u003e\n \u003cp\u003e(200\u0026ndash;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYesilirmak (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;3288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks, clastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346\u0026ndash;1108\u003c/p\u003e\n \u003cp\u003e(400\u0026ndash;600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;822\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;250)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKizilirmak (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;3857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221\u0026ndash;845\u003c/p\u003e\n \u003cp\u003e(400\u0026ndash;500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;772\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKonya Endorheic (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e899\u0026ndash;3405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263\u0026ndash;1113\u003c/p\u003e\n \u003cp\u003e(400\u0026ndash;500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;862\u003c/p\u003e\n \u003cp\u003e(100\u0026ndash;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Mediterranean (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;3487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370\u0026ndash;700\u003c/p\u003e\n \u003cp\u003e(500\u0026ndash;600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1395\u003c/p\u003e\n \u003cp\u003e(200\u0026ndash;250)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeyhan (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;3683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks, continental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316\u0026ndash;1062\u003c/p\u003e\n \u003cp\u003e(300\u0026ndash;600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1437\u003c/p\u003e\n \u003cp\u003e(200\u0026ndash;250)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower Asi (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;2201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e493\u0026ndash;980\u003c/p\u003e\n \u003cp\u003e(700\u0026ndash;900)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1262\u003c/p\u003e\n \u003cp\u003e(100\u0026ndash;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCeyhan (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;3058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e317\u0026ndash;1628\u003c/p\u003e\n \u003cp\u003e(600\u0026ndash;800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1147\u003c/p\u003e\n \u003cp\u003e(100\u0026ndash;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper Euphrates (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e317\u0026ndash;3838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254\u0026ndash;1259\u003c/p\u003e\n \u003cp\u003e(400\u0026ndash;600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1114\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Black Sea (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;3776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolcanic and sedimentary rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401\u0026ndash;2594\u003c/p\u003e\n \u003cp\u003e(900\u0026ndash;1100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026ndash;1102\u003c/p\u003e\n \u003cp\u003e(200\u0026ndash;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper Coruh (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u0026ndash;3893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311\u0026ndash;2038\u003c/p\u003e\n \u003cp\u003e(400\u0026ndash;500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;24\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026ndash;1027\u003c/p\u003e\n \u003cp\u003e(200\u0026ndash;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper Aras (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e792\u0026ndash;5100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227\u0026ndash;778\u003c/p\u003e\n \u003cp\u003e(400\u0026ndash;600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1961\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;250)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLake Van Endorheic (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1638\u0026ndash;4029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks, continental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e358\u0026ndash;912\u003c/p\u003e\n \u003cp\u003e(400\u0026ndash;500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;23\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;753\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper Tigris (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333\u0026ndash;3935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299\u0026ndash;1887\u003c/p\u003e\n \u003cp\u003e(900\u0026ndash;1000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026ndash;24\u003c/p\u003e\n \u003cp\u003e(12 and 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1293\u003c/p\u003e\n \u003cp\u003e(150\u0026ndash;300)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eUtilizing the Analytic Hierarchy Process\u003c/h2\u003e\n \u003cp\u003eWithin the AHP framework, pairwise comparison matrices were carefully constructed for each study basin. Factor weights were subsequently computed for both the eight-factor and ten-factor methods, as detailed in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. To rigorously evaluate the appropriateness of the assigned scores, a quantitative analysis was performed considering the distribution of historical landslide occurrences across data layer categories. This analysis is summarized in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, which shows the percentage of pixel counts for each category. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides a visual representation of the resulting unclassified landslide susceptibility maps for each basin generated using both of the factor sets.\u003c/p\u003e\n \u003cp\u003eThe overlay of historical landslide polygons on each causal factor layer allowed for the extraction of relevant data values, facilitating an objective evaluation of the assigned rating values. Each factor was segmented into subclasses, and linear normalization (min-max feature scaling) was applied to standardize values within a range of 0 to 1. Aggregating the accumulated layer parameters for both the eight- and ten-factor models derived two distinct landslide susceptibility maps, as visually depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Notably, the pixel values within these maps spanned the entire spectrum from 0 to 1, providing a comprehensive representation of landslide susceptibility within the respective basins.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"619\" height=\"439\"\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of the historical landslide areas index (percent of pixel) in regards to various data layer classes for the Western Mediterranean basin (please refer to the details of the remaining 25 drainage basins in the Supplementary Material Section as Online Resource 2)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData layer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLAI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData layer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLAI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026deg; \u0026minus;\u0026thinsp;10\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eLithology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYoung deposits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026deg; \u0026minus;\u0026thinsp;15\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e50.51\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026deg; \u0026minus;\u0026thinsp;20\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGabbro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026deg; \u0026minus;\u0026thinsp;25\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinental clastic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026deg; \u0026minus;\u0026thinsp;30\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026deg; \u0026minus;\u0026thinsp;35\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClastic and carbonate rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e29.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026deg; \u0026minus;\u0026thinsp;40\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimestone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"13\"\u003e\n \u003cp\u003eInternal relief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u0026ndash;150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetamorpic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u0026ndash;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOphiolitic rocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u0026ndash;250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e29.14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u0026ndash;300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u0026ndash;350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350\u0026ndash;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u0026ndash;450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450\u0026ndash;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u0026ndash;550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.89\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e550\u0026ndash;600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e600\u0026ndash;650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eLandform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanyons, deeply incised streams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e650\u0026ndash;700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMidslope drainages, shallow valleys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u0026ndash;600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e32.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpland drainages, headwaters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e600\u0026ndash;700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU-shaped valleys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e700\u0026ndash;800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800\u0026ndash;900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOpen slopes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e68.23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e900\u0026ndash;1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper slopes, mesas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u0026ndash;1100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal ridges/hills in valleys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eTWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMidslope ridges, small hills in plains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eCurvature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV / V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u0026ndash;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV / S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026ndash;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV / X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026ndash;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS / V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026ndash;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eS / S\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.71\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLand cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgricultural areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS / X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e44.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eX / V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSemi natural areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eX / S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEarthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZone 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e67.31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eX / X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZone 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLandslides are a significant natural hazard within the geographical boundaries of T\u0026uuml;rkiye. Several contributing factors, such as channel incision, seismic activity, heavy precipitation, and anthropogenic influences, collectively underscore their importance as recurrent events. For the purpose of conducting AHP-based studies, as elaborated in previous sections, a careful selection of causal and triggering factors has been undertaken. This study has assessed various factors deemed important for initiating landslides, including lithology, land cover, internal relief, slope, aspect, classified landforms, classified curvature, and topographic wetness index (TWI). At the same time, rainfall and seismic activity were identified as critical triggering factors, providing the basis for the extensive analysis conducted herein.\u003c/p\u003e \u003cp\u003eThe rationale for choosing a total of ten factors in the study was two-folds. First, these factors were chosen because of their widespread availability in the public domain and their established utility in landslide susceptibility research efforts. Second, to allow for a detailed and nuanced analysis, the selected factors were categorized into two distinct groups: one set of ten factors and another set of eight factors.\u003c/p\u003e \u003cp\u003eDue to their minimal impact on landslide development in areas of moderate susceptibility, aspect and rainfall were deliberately excluded from the ten-factor analysis. In particular, aspect was found to play a relatively minor role in influencing landslide susceptibility throughout the study basins. In addition, the precipitation factor, which denotes the annual mean total precipitation, represents the arithmetic mean of the annual recorded precipitation. While it provides insight into the annual precipitation received, it lacks the granularity necessary to measure rainfall intensity relative to the threshold for potential landslide occurrence. Extreme rainfall events with the potential to trigger landslides may not be accurately captured by the arithmetic averaging of annual rainfall data.\u003c/p\u003e \u003cp\u003eIn this context, a careful landslide hazard assessment was carried out for each drainage basin using the AHP methodology. Two different approaches, namely the 8-factor and 10-factor models, were used to comprehensively investigate the susceptibility within each basin. Each basin was thoroughly examined and individual results are presented in Online Resource 3, as previously detailed by Okalp (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Okalp and Akg\u0026uuml;n (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLandslide inventory maps, crucial for validating the study's findings, were sourced from 1:500,000 scale maps published by the General Directorate of Mineral Research and Exploration of T\u0026uuml;rkiye (MTA) within the past decade. These authoritative maps were digitized to delineate historical landslide polygons. The polygons were then systematically overlain on the generated landslide susceptibility maps, which incorporated either eight or ten factors. Finally, for each susceptibility map, a comprehensive analysis of pixel counts, both within and outside the landslide areas, was performed to facilitate consistent evaluation.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea illustrates the distribution of landslide susceptibility in the western Mediterranean basin under two scenarios: using eight-factor and ten-factor based maps. The figure shows pixel counts categorized as inside and outside historical landslide polygons. The vertical axes represent these pixel count distributions. A key challenge is the disparity in the range of pixel count values between the inner and outer landslide areas. This makes it difficult to visualize both distributions in a single figure. To address this issue, the peak locations for each scenario (eight and ten factors) were highlighted on a unified horizontal line labeled ''E-line'' in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, as suggested by Okalp (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Okalp and Akg\u0026uuml;n (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While the peaks themselves may differ, this approach facilitated a comparative analysis of the overall distribution patterns.\u003c/p\u003e \u003cp\u003eAfter generating two landslide susceptibility maps - one based on eight factors and the other based on ten factors - a critical challenge emerged in the selection of an optimal map for the specific basin studied. Ideally, the most appropriate map would be reflected by the corresponding histogram curves. The ideal histogram for pixel values within historical landslide polygons would have a pronounced positive skew, with its peak (on the x-axis) approaching 1. Conversely, the optimal histogram for pixel values outside of the landslide polygons would exhibit a negative skew, with its peak value approaching 0. This established criterion, based on the distribution tails of the histograms, served as the primary basis for the selection of the eight-factor and ten-factor maps (Okalp, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInspection of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea reveals that the peak values of the histograms from both scenarios, derived from pixel counts within historical landslide polygons, were closely aligned. Moreover, the peak value of the eight-factor histogram for areas outside historical landslide polygons showed a slightly stronger negative skew, nearing a value of 0. However, these subtle differences in the histogram curves were deemed insufficient to decisively select the optimal map (eight-factor vs. ten-factor) for the basin under study. To resolve this ambiguity and facilitate data-driven selection, Receiver Operator Characteristic (ROC) curve analysis was incorporated into the evaluation process as a subsequent step (Aditian et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo objectively select the optimal landslide susceptibility map, a rigorous evaluation using ROC curves was performed. ROC curves provide a comprehensive assessment of model performance across all classification thresholds. The area under the ROC curve (AUC) is utilized as a quantitative indicator of the model's overall accuracy. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, a comparative analysis of the ROC curves revealed superior predictive power for the ten-factor model. This was evidenced by the larger AUC associated with the ten-factor ROC curve. In addition, the ten-factor ROC curve showed a trajectory closer to the upper left corner of the graph, indicating a stronger ability to discriminate between landslide and non-landslide pixels (Fawcett, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Therefore, based on the robust performance metrics provided by the ROC analysis, the ten-factor model was selected for this particular basin.\u003c/p\u003e \u003cp\u003eLandslide susceptibility maps, along with landslide hazard maps, are often reclassified into a manageable number of classes (typically three to five) to facilitate interpretation. However, a significant obstacle for this process in this study was the substantial variability observed in the susceptibility maps produced for each individual basin. This variability precluded the application of generic classification thresholds across all maps. As a result, each map required independent determination of appropriate thresholds and classification schemes. Although several synthetic classification methods were explored, none provided entirely satisfactory results. In particular, the application of popular techniques such as Jenks' natural breaks, the quantile method, and the geometric interval method resulted in inconsistent thresholds, especially for the \"very high\" susceptibility subclass. Interestingly, the application of these methods identified the western Mediterranean basin as \"very high landslide prone\", a finding that contradicts the documented moderate landslide activity in the region. This discrepancy underscores the challenges in effectively using standard procedures to manage the synthetic classification of susceptibility maps produced in this research.\u003c/p\u003e \u003cp\u003eAn innovative and highly subjective methodology was employed to synthetically classify the maps generated in this study (Okalp \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Okalp and Akg\u0026uuml;n \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Initially, the peaks from both the inner and outer landslide polygons were aligned on a singular axis (line E) in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, independent of their actual magnitudes. The peak value (point A) on the histogram curve, representing pixel counts from areas outside the landslide zones, was used as the initial threshold to delineate between the \"no\" and \"low\" susceptibility classes.\u003c/p\u003e \u003cp\u003eSubsequently, the intersection (point B) of the histogram curves for the outer and inner landslide polygons was established as the secondary threshold, differentiating the \"Low\" and \"Moderate\" susceptibility classes. Furthermore, the peak (point C) on the histogram curve for the inner landslide pixels was designated as the tertiary threshold, distinguishing between the \"Moderate\" and \"High\" classes. Lastly, the midpoint (point D) between point C and a fixed value of 1.0 was set as the quaternary threshold to separate the \"High\" and \"Very High\" susceptibility classes. This complex classification process was systematically applied to categorize the landslide susceptibility of the Western Mediterranean basin, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec. Other basins were evaluated independently, with their corresponding results detailed in tables and graphs presented in the Supplementary Material Section as Online Resource 4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the generated landslide susceptibility maps\u003c/h2\u003e \u003cp\u003eThe Analytic Hierarchy Process (AHP) enables the translation of qualitative concerns into quantifiable metrics, providing an invaluable tool for multi-criteria decision analysis problems. It skillfully transforms subjective judgments into objective data, thereby increasing the robustness of decision processes. In the context of landslide susceptibility analysis, the lack of established or fixed values for weights and ratings corresponding to different factors is further emphasized (Okalp \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This lack of standardization necessitates the use of objective analysis to determine these values, rather than relying on subjective expert opinion.\u003c/p\u003e \u003cp\u003eThe Analytic Hierarchy Process (AHP) uses historical landslide footprints to determine rating values and factors influencing landslide occurrence. Overlaying these footprints with unnormalized values for each factor supports a detailed and unbiased assessment, thereby improving landslide hazard analysis.\u003c/p\u003e \u003cp\u003eThe study revealed several key factors influencing landslide susceptibility across various basins. Slope exhibited a non-linear relationship, with the highest landslide frequencies concentrated between 5\u0026deg; and 15\u0026deg;, indicating a critical range for susceptibility assessments. Moderate internal relief (200\u0026ndash;250 m/km\u0026sup2;) emerged as a prominent factor associated with increased landslide activity.\u003c/p\u003e \u003cp\u003eNotably, historic landslides in the study areas clustered within a specific range of Topographic Wetness Index (TWI) values. A significant proportion occurred within a TWI layer where values of 12 and 13 were extracted from a DEM with a 90 m resolution. Because topography affects water movement in sloping terrain, the TWI effectively quantifies the effect of local topography on hydrologic processes. This provides insight into soil moisture distribution and surface saturation. Incorporation into the TOPMODEL which is a distributed hydrological model that aids in defining hydrological similarity, highlights the importance of TWI in modeling topography-driven processes at hillslope and basin scales.\u003c/p\u003e \u003cp\u003eFurther analysis showed that about half of the basins possessed TWI values of 12\u0026ndash;13, which fell at the midpoint of the TWI distribution. This suggested a potential threshold of TWI 12 for landslide initiation within the study basins at this resolution.\u003c/p\u003e \u003cp\u003eAs expected, historic landslides occurred predominantly in areas with open and planar slope (S/S) curvature types. However, the lack of significant aggregation across precipitation and aspect distributions within the study regions necessitated the use of the Analytic Hierarchy Process (AHP). This involved a two-pronged analysis for each basin, employing both 8-factor and 10-factor layers.\u003c/p\u003e \u003cp\u003eHistorical data analysis within the study regions showed that agricultural areas and forests were more susceptible to landslides, potentially exacerbated by land-use changes like deforestation. Additionally, a significant portion of historical landslides have occurred within Earthquake Zone 1, highlighting its role as a triggering factor. Regarding lithology, clastic and carbonate rock formations have been identified as being most prone to landslides due to their extensive history of such events and their inherent geological properties.\u003c/p\u003e \u003cp\u003eRainfall intensity, particularly in specific regions, emerged as a primary trigger for landslides, underlining the crucial impact of precipitation on slope stability. Aspect had a minimal influence on susceptibility across the study basins. Specific landforms like U-shaped valleys and open slopes exhibited a clear association with increased landslide frequency.\u003c/p\u003e \u003cp\u003eAfter producing unclassified landslide susceptibility maps for the basins using both 8-factor and 10-factor approaches, a selection process was carried out as previously described. Comparative analyses showed that the 10-factor approach performed better in 9 out of 26 basins, namely Marmara, Buyuk Menderes, Western Mediterranean, Akarcay Endorheic, Western Black Sea, Yesilirmak, Kizilirmak, Upper Euphrates and Eastern Black Sea basins.\u003c/p\u003e \u003cp\u003eThe decision between the 8-factor and 10-factor methods was made by analyzing pixel distributions within historical landslide zones (inner and outer) and examining Receiver Operating Characteristic (ROC) curves. The main criterion was to prioritize peaks of the outer landslide histogram closest to 0 and the inner landslide histogram closest to 1, using ROC curve values as a secondary criterion. When histogram data were inconclusive, the area under the ROC curve was reviewed.\u003c/p\u003e \u003cp\u003eThe results revealed significant variations in ROC curves across the basins. Several basins achieved impressive AUC values, exceeding 0.7, indicating strong performance in predicting landslides. For example, the Lower Maritsa-Ergene Basin exhibited a remarkable AUC of 0.8709, highlighting its exceptional predictive capability. Similarly, the Akarcay Endorheic Basin reached a high AUC of 0.8421, showcasing its effectiveness in landslide susceptibility assessment. Conversely, some basins presented lower AUC values. The Upper Tigris Basin, for instance, yielded an AUC of 0.606, suggesting a need for further investigation or refinement for this basin. Similarly, the Sakarya Basin displayed a lower AUC of 0.5672, indicating that additional data or adjustments could enhance its predictive performance for this basin. The selected unclassified landslide susceptibility maps, developed using the AHP with either 8 or 10 factors, consistently classified the basins into five distinct groups, as outlined earlier. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes these group distributions.\u003c/p\u003e \u003cp\u003eThe Analytic Hierarchy Process (AHP) is a recognized technique for assessing landslide susceptibility and serves as a benchmark in current research, particularly when evaluating against machine learning algorithms. A study by Huang et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in Shicheng County, China, assessed various models including heuristic AHP, statistical approaches, and machine learning techniques like Binary Logistic Regression, Multilayer Perceptron, Backpropagation Neural Network, Support Vector Machine, and C5.0 Decision Tree. The results demonstrated the effectiveness of all models, with the C5.0 Decision Tree achieving the highest accuracy with an AUC of 0.868, suggesting a potential for refining AHP-based results (AUC of 0.773) through integrating machine learning, neural networks, fuzzy logic, and other soft computing techniques.\u003c/p\u003e \u003cp\u003eAnalysis of the \"very high\" landslide susceptibility zone, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, revealed that in most basins the spatial extent of this zone exceeded that of the corresponding historical landslide footprint, highlighting the ability of the method to identify potentially landslide-prone areas beyond known locations.\u003c/p\u003e \u003cp\u003eThe focus was on the combined area classified as \"high\" and \"very high\" landslide susceptibility within each basin (last columns of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), as compared to the historical landslide distribution. Ideally, the \"very high\" susceptibility area should exceed the documented historical landslide footprint for the corresponding basin.\u003c/p\u003e \u003cp\u003eWhile most basins met this expectation, six basins, notably Akarcay Endorheic, Western Black Sea, and Upper Euphrates, showed a smaller \"very high\" susceptibility zone compared to the historical landslide area. This discrepancy is likely due to the threshold values used for classifying landslide susceptibility maps, which may have underestimated susceptibility in these basins.\u003c/p\u003e \u003cp\u003eThough a synthetic reclassification approach could address this underestimation, it falls outside the scope of this study as all basins were classified using the standardized procedure. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e has been expanded to include an additional column displaying the combined area percentage of \"high\" and \"very high\" landslide susceptibility zones. Except for the Akarcay Endorheic Basin, the combined area of these susceptibility classes exceeded the historical landslide area for each basin. The minimal difference in the Akarcay Endorheic Basin may be considered negligible for landslide susceptibility assessment at a 1:500,000 scale.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSynthetically classified landslide susceptibility zone distributions of basins and comparison of synthetically classified highly landslide susceptible zone areas with historical landslides\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBasin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSelected factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePixel counts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e \u003cp\u003eClassified landslide susceptibility zones vs Historical LS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh\u0026thinsp;+\u0026thinsp;Very high\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Maritsa-Ergene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1783136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07% \u0026gt; 0.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.17% \u0026gt; 0.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2764674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.18% \u0026gt; 1.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.56% \u0026gt; 1.89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusurluk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2938688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.98% \u0026gt; 0.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.99% \u0026gt; 0.59%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Aegean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1223660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.10% \u0026gt; 0.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.08% \u0026gt; 0.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGediz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2050888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.28% \u0026gt; 0.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.80% \u0026gt; 0.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKucuk Menderes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e847415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12% \u0026gt; 0.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.85% \u0026gt; 0.08%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuyuk Menderes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3155909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.26% \u0026gt; 0.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.82% \u0026gt; 0.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Mediterranean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2552371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.26% \u0026gt; 1.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.94% \u0026gt; 1.42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntalya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2492362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.71% \u0026gt; 0.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.21% \u0026gt; 0.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurdur Endorheic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e724849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.66% \u0026gt; 0.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.84% \u0026gt; 0.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkarcay Endorheic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e932025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21% \u0026lt; 2.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.97% \u0026lt; 2.81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSakarya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7412864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.98% \u0026gt; 1.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.44% \u0026gt; 1.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Black Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3568596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.59% \u0026lt; 10.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.96% \u0026gt; 10.56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYesilirmak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4863497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.94% \u0026gt; 4.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.17% \u0026gt; 4.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKizilirmak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9863497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.11% \u0026gt; 2.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.73% \u0026gt; 2.01%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKonya Endorheic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5928017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59% \u0026gt; 0.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.21% \u0026gt; 0.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Mediterranean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2656410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.82% \u0026lt; 2.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.98% \u0026gt; 2.24%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeyhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2655827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.70% \u0026gt; 0.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.63% \u0026gt; 0.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Asi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e947267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.76% \u0026gt; 0.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.71% \u0026gt; 0.35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeyhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2595952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.84% \u0026gt; 0.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.05% \u0026gt; 0.77%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Euphrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14484190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.84% \u0026lt; 4.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.97% \u0026gt; 4.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Black Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2813106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.77% \u0026lt; 2.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.08% \u0026gt; 2.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Coruh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2495302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.22% \u0026lt; 5.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.56% \u0026gt; 5.01%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Aras\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3420905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.30% \u0026gt; 4.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.54% \u0026gt; 4.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLake Van Endorheic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2210789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.13% \u0026gt; 2.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.94% \u0026gt; 2.08%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Tigris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6426892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.19% \u0026gt; 2.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.05% \u0026gt; 2.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConclusions and recommendations\u003c/h2\u003e \u003cp\u003eThis study has conducted a comprehensive analysis of landslide susceptibility mapping in mid-sized regions using publicly available datasets and a GIS-based semi-quantitative approach. The study systematically outlined an established framework for mapping landslide susceptibility. Using the Analytical Hierarchy Process (AHP), which is an accepted semi-quantitative method, the research applied this technique to the study basins by using both eight- and ten-variable models.\u003c/p\u003e \u003cp\u003eThe study included rigorous validation and evaluation processes, including histogram and ROC curve analyses, resulting in factor-based maps for each basin. As a result, the research produced accurately constructed 1:500,000 scale landslide susceptibility maps for the designated regions. This work stands as a pioneering effort in systematically assessing landslide susceptibility across the major drainage basins of T\u0026uuml;rkiye. By encompassing all these basins, the study effectively provides the first comprehensive assessment of landslide susceptibility for the entire country. This basin-wide approach offers a more nuanced understanding of the factors influencing landslide occurrence compared to previous national-scale studies that might rely on less detailed data. The findings not only improved our knowledge of landslide susceptibility in T\u0026uuml;rkiye but also established an invaluable framework for future susceptibility assessments in other geologically diverse regions.\u003c/p\u003e \u003cp\u003eThe methodology developed for semi-quantitative landslide susceptibility zoning provided important insights for decision-makers involved in regional-scale assessment of landslide susceptibility, hazards and risks, and is potentially applicable at both national and continental scales.\u003c/p\u003e \u003cp\u003eScale and pixel size are recognized as critical elements in landslide susceptibility analysis. Their selection must prioritize practicality and ensure alignment with the capabilities of available hardware and software. This is particularly important when processing high-resolution datasets covering large areas. Given these constraints, the study identified a 90-meter pixel resolution and a 1:500,000 scale as optimal for investigating landslide susceptibility over large regions. Results indicated that the 8-factor approach produced superior results in 17 of the 26 basins analyzed.\u003c/p\u003e \u003cp\u003eA significant number of historical landslides were noted within the Topographic Wetness Index (TWI) layer, particularly at TWI values of 12 and 13, calculated from a 90-meter resolution digital elevation model (DEM). TWI is highly regarded as an important metric for modeling topographic influences at the hillslope or drainage basin scale. Detailed analysis of the distribution of TWI across the basins identified a value of 12 as the critical threshold for landslide initiation at the 90-meter pixel resolution used.\u003c/p\u003e \u003cp\u003eIn the basin-specific assessments, curvature, landform, and seismic activity emerged as the primary controlling factors, collectively accounting for approximately 50% of the influence on landslide susceptibility (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The curvature and landform layers, which are terrain derivatives, were extracted directly from the DEM. These factors are significantly influenced by lithology, climatic conditions, and seismic activity, and serve as critical indicators of landslide potential. This demonstrates a strong alignment between digital modeling and field-based analytical methods for understanding and predicting landslide activity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeights of factors obtained for basins (governing factors are highlighted in grey color)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInt. relief\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLithology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLand cov.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEarthq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTWI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLandform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCurvatur.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLo. Mar-Erg.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLo. Mar-Erg.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarmara\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.24%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarmara\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.02%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusurluk\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusurluk\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNort. Aegean\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNort. Aegean\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGediz\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGediz\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK. Menderes\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK. Menderes\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB. Menderes\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB. Menderes\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest. Med.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.86%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest. Med.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntalya\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntalya\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurdur End.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.27%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurdur End.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkarcay End \u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkarcay End \u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSakarya\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSakarya\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest. Bl. Sea\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest. Bl. Sea\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYesilirmak\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYesilirmak\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.68%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKizilirmak\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.23%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKizilirmak\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKonya End.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKonya End.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast. Med.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast. Med.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeyhan\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeyhan\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Asi\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Asi\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeyhan\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.74%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeyhan\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.93%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp. Euphrates\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp Euphrates\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.49%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast. Bl. Sea\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.03%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast. Bl. Sea\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Coruh\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Coruh\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Aras\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Aras\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLk. Van End.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLk.Van End. \u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Tigris\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Tigris\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003e8\u003c/sup\u003e Basin results for 8-factor based analysis\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003e10\u003c/sup\u003e Basin results for 10-factor based analysis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe methodology used in this study to evaluate mid-scale landslide susceptibility mapping in mid-sized regions provides a valuable framework for future efforts focused on the development of comprehensive nationwide landslide susceptibility maps. However, direct implementation of sophisticated modeling techniques for national or continental-scale landslide susceptibility assessment, which require larger data sets, finer pixel resolution, and greater computational power, remains impractical due to current hardware and software limitations.\u003c/p\u003e \u003cp\u003eThese factors were incorporated into a comprehensive analysis that resulted in the development of landslide susceptibility maps for the study regions. The semi-quantitative model employed facilitated the generation of histogram and Receiver Operating Characteristic (ROC) curves. These curves were then subjected to rigorous evaluation and reclassification using a novel methodological approach. This process culminated in the careful construction and robust validation of several landslide susceptibility maps within the study areas. Guided by our findings, we propose the following recommendations for future landslide susceptibility research:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePrioritize open data accessibility\u003c/b\u003e: This research utilized open-source or publicly accessible datasets. Hard copies of landslide inventory maps, geological maps, and rainfall data were obtained from relevant government agencies and subsequently digitized into vector formats. Additional datasets were sourced from CGIAR-CSI and the European Environment Agency.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eUtilization of causal factors from DEM\u003c/b\u003e: The study utilized the Digital Elevation Model (DEM), from which most of the causal factors, including internal relief, slope, aspect, classified landforms, classified curvature, and topographic wetness index (TWI), were derived. This highlights the critical role of the DEM in landslide susceptibility assessment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eApplication of Multi-Factor Approaches\u003c/b\u003e: The research involved the application of both eight-factor and ten-factor based approaches within a semi-quantitative framework.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInnovation in Landslide Susceptibility Maps\u003c/b\u003e: A groundbreaking technique for synthetic classification of the generated landslide susceptibility maps within the semi-quantitative model was implemented.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFuture evaluation of the TWI variable\u003c/b\u003e: Future studies are intended to comprehensively evaluate the Topographic Wetness Index (TWI) at various pixel resolutions and scales to determine whether a TWI value of 12 is a viable threshold for assessing landslide susceptibility.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eApplicability of Study Methodology\u003c/b\u003e: The study's comprehensive methodology demonstrates the potential for transferability to landslide susceptibility mapping over large geographic regions, including countries or continents.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImpact of DEM resolution\u003c/b\u003e: The increase in DEM resolution plays a critical role, potentially revealing hidden relationships or rules within coarse-resolution DEMs.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStrategic partitioning for area analysis\u003c/b\u003e: For extensive landslide susceptibility analyses, strategic subdivision of the study area into smaller sub-basin boundaries is recommended. This approach allows for a discrete and more accurate analysis of each basin and promotes a deeper understanding of the unique basin characteristics that influence landslides.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExpanded Subdivision for Advanced Study\u003c/b\u003e: To facilitate more detailed analysis in the future, it is suggested that the study basins be subdivided into sub-basins. This, combined with finer pixel resolution and larger scales, will result in a more accurate and comprehensive assessment of landslide susceptibility.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis study presents the first comprehensive assessment of landslide susceptibility for T\u0026uuml;rkiye, analyzing various factors such as slope, topography, and land cover across all major drainage basins. The research provided valuable insights for future susceptibility assessments. Successful implementation of such detailed studies requires robust interagency coordination. This coordination facilitates the strategic allocation of qualified personnel, time, financial resources, and reliable data sets, thereby fostering significant progress in the field of landslide hazard assessment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKO and HA: conceived the idea for the manuscript. KO: collected datasets, analyzed, compiled the GIS maps, and drafted the manuscript. HA: provided supervision, verification, editing, and modification. KO and HA: collaborated in finalizing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Middle East Technical University (METU) Research Fund Project No. BAP-03-09-2010-01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital elevation model was sourced from the CGIAR-CSI website (http://srtm.csi.cgiar.org), the CORINE Land Cover 2018 seamless vector data was sourced from the CLC website (https://land.copernicus.eu/pan-european/corine-land-cover/clc2018), the 1:500,000 scale hard-copy geological maps and landslide inventory maps were purchased from the Turkish Mineral Research and Exploration General Directorate, the rainfall records, the rainfall records were compiled from the archives of the Turkish State Meteorological Service, the earthquake data layer was digitized from the Earthquake Map of T\u0026uuml;rkiye published by the Disaster and Emergency Management Authority, landslide statistics were compiled from the Disaster and Emergency Management Authority (https://www.afad.gov.tr/kurumlar/afad.gov.tr/35429/xfiles/Turkiye_de_Afetler.pdf), and GoogleEarth software (https://earth.google.com).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that we do not have any commercial or associative interest that represents a conflict of interest or competing interest in connection with the work submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbella EAC, van Westen CJ (2007) Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation. 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Accessed 3 Feb 2013\u003c/li\u003e\n\u003cli\u003evan Westen CJ (1997) Statistical landslide hazard analysis. ILWIS 2.1 for Windows Applications Guide, ITC Publication, Enschede, pp. 73\u0026ndash;84\u003c/li\u003e\n\u003cli\u003eYalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena, 72: 1\u0026ndash;12. https://doi.org/10.1016/j.catena.2007.01.003\u003c/li\u003e\n\u003cli\u003eYoshimatsu H, Abe S (2006) A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method. Landslides, 3, 149\u0026ndash;158. https://doi.org/10.1007/s10346-005-0031-y\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Landslide Susceptibility, Semi-Quantitative, Analytical Hierarchy Process (AHP), Drainage Basin Scale, AHP-GIS Integration, Türkiye","lastPublishedDoi":"10.21203/rs.3.rs-4704929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4704929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study uses the Analytical Hierarchy Process (AHP) integrated with Geographic Information Systems (GIS) to assess landslide susceptibility in T\u0026uuml;rkiye at a drainage basin scale. T\u0026uuml;rkiye's mountainous terrain makes it highly prone to landslides, especially due to heavy rainfall from climatic changes. Addressing the need for comprehensive assessments, the study integrates multiple parameters such as slope, lithology, internal relief, land cover, and rainfall intensity using publicly accessible datasets such as digital elevation models (DEM), geological maps, and rainfall records. The first comprehensive landslide susceptibility map for T\u0026uuml;rkiye was produced, validated through histogram and ROC curve analyses, resulting in 1:500,000 scale maps for each basin. This assessment offers a detailed understanding of landslide occurrences, identifying that moderate slopes (5\u0026deg;-15\u0026deg;) and internal relief (200\u0026ndash;250 m/km\u0026sup2;) significantly influence landslides. A potential threshold for the Topographic Wetness Index (TWI) at 12\u0026ndash;13 was also identified. The study highlights the importance of DEM resolution and strategic area subdivision for detailed analysis. The maps and classification methods provide a valuable framework for future research, enhancing understanding of geological hazards and aiding decision-makers in managing landslide risks. Recommendations include prioritizing open data, evaluating TWI, and refining classification methods for improved accuracy.\u003c/p\u003e","manuscriptTitle":"Assessing Landslide Susceptibility of Türkiye at Drainage Basin Scale: A Semi-quantitative Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-01 17:32:59","doi":"10.21203/rs.3.rs-4704929/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b1f713be-7a89-4273-a691-4fd9ab7c7505","owner":[],"postedDate":"August 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-15T04:09:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-01 17:32:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4704929","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4704929","identity":"rs-4704929","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}