Application of Geospatial and Frequency Ratio Techniques in Landslide Susceptibility Mapping: Case Study of Daramalo District, Ethiopia

Application of Geospatial and Frequency Ratio Techniques in Landslide Susceptibility Mapping: Case Study of Daramalo District, Ethiopia | 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 Application of Geospatial and Frequency Ratio Techniques in Landslide Susceptibility Mapping: Case Study of Daramalo District, Ethiopia Yonas Oyda, Hailu Regasa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5154634/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 Daramalo district, located in the Gamo Zone of South Ethiopia, is one of the areas most affected by landslides. This study aims to assess the landslide susceptibility of the area and to develop a comprehensive landslide susceptibility map. To achieve this, a bivariate statistical frequency ratio model was employed. A detailed inventory of landslides was compiled through fieldwork and the interpretation of Google Earth imagery, identifying a total of 32 landslides. These were categorized into training landslides (70%) for model development and validation landslides (30%) for model evaluation. Eight causative factors slope, aspect, elevation, curvature profile, drainage density, lithology, lineament density, and land use/land cover (LULC) were integrated with the training landslide data to determine the frequency ratio values for each class of these factors. Relative frequency values were assigned to the appropriate factor classes, which were then summed using a raster calculator algorithm to produce the landslide susceptibility map. The final susceptibility map indicates that 44% (110 km²) of the study area is classified as low susceptibility, 36.8% (92 km²) as moderate susceptibility, and 19.2% (48 km²) as high susceptibility. This suggests that approximately 20% of the area is at significant hazard of landslides, while about 80% has relatively low to moderate susceptibility to this natural hazard. The performance of the frequency ratio model was validated using the receiver operating characteristic (ROC) curve, achieving a notable success prediction rate of 89.03%. Overall, the model demonstrated strong accuracy. The resulting map is anticipated to be a valuable resource for land use planning, site selection, and the formulation of effective landslide prevention and mitigation strategies. Landslide susceptibility Frequency ratio model Daramalo District Gamo Zone Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Landslides are among the most destructive natural hazards, particularly in mountainous regions, causing significant loss of life, damage to infrastructure, and environmental degradation globally [ 1 ], [ 2 ]. These complex natural phenomena are often triggered by natural factors such as rainfall, snowmelt, or earthquakes, but they can also result from human activities, including improper road construction and deforestation [ 3 ], [ 4 ]. To mitigate the impacts of landslides, governments, and research institutions have increasingly focused on landslide susceptibility mapping, which identifies areas at risk of landslide events. By doing so, they aim to inform disaster prevention strategies and reduce potential damage [ 5 ]–[ 7 ]. Ethiopia, with its vast highland regions and variable climate, is highly susceptible to landslides, particularly during the rainy season when the annual precipitation exceeds 1200 mm in many areas [ 8 ], [ 9 ]. Approximately 60% of the Ethiopian population lives in these highlands, making landslide events a significant threat to both lives and livelihoods. Historical records show instability in the superficial materials and bedrock in several parts of the northern, southern, and western Ethiopian Plateau [ 10 ], [ 11 ]. Despite this, landslide hazard susceptibility mapping remains limited in many regions, including the Daramalo district of the Gamo Highlands, where this study is focused. Several techniques have been developed to identify and map landslide susceptibility zones, each with its advantages and limitations. The multi-influencing factor (MIF) approach integrates various factors like slope, lithology, and land use to predict landslide-prone areas. This method, while comprehensive, requires a detailed dataset, which can be challenging to obtain in certain regions [ 12 ]–[ 14 ]. Weight of Evidence (WoE) is another widely used statistical method that evaluates the relationship between landslide occurrences and conditioning factors, providing a probabilistic measure of susceptibility [ 1 ], [ 15 ]–[ 17 ]. AHP (Analytic Hierarchy Process) relies on expert judgment to assign weights to different factors, which can introduce subjectivity but is effective in areas where empirical data is limited [ 18 ]–[ 20 ]. The Frequency Ratio (FR) method, often paired with geospatial analysis, calculates the ratio of landslide occurrences to the presence of specific conditioning factors, making it a robust method for large-scale mapping [ 6 ], [ 11 ], [ 16 ], [ 21 ]. Logistic regression, on the other hand, models the probability of landslide events based on the combination of multiple factors, offering a statistical perspective on susceptibility mapping [ 2 ], [ 9 ], [ 22 ] Remote sensing and GIS tools have also played a critical role in advancing landslide studies, allowing for the efficient analysis of large datasets and the production of high-resolution maps [ 4 ], [ 23 ]. Each of these methods has been employed in various global contexts, contributing valuable insights into the prediction and prevention of landslides. The present study employs geospatial analysis and the Frequency Ratio method techniques for landslide susceptibility mapping in the Daramalo district, Gamo Highlands. These methods were selected for their ability to handle complex topography data and diverse conditioning factors, such as slope, lithology, and land use. The Frequency Ratio method, in particular, is well-suited for the region due to its capacity to incorporate historical landslide data and produce statistically reliable susceptibility maps [ 24 ], [ 25 ]. Geospatial analysis, combined with GIS, enables the efficient management of large datasets, making it a powerful tool for regional-scale mapping in data-scarce areas like Daramalo. The Daramalo district, located in the Gamo Highlands of southern Ethiopia, is characterized by steep slopes and complex geological structures, making it particularly susceptible to landslides. Despite this, no comprehensive landslide hazard studies have been conducted in the area, and no previous research has applied the geospatial and Frequency Ratio methods for susceptibility mapping. The study area represents an appropriate location for applying these techniques for the first time due to the incorporation of a variety of topographical and geological conditions. The primary objective of this study is to develop a landslide susceptibility map for the Daramalo district using geospatial analysis and the Frequency Ratio method. To identify and evaluate the key factors contributing to landslide occurrences in the Daramalo district, including topography, slope, lithology, land use, and, assign relative weights to these factors using the Frequency Ratio method. Furthermore, the study aims to develop a landslide susceptibility map by integrating these weighted factors in a GIS environment, validating the map using known landslide locations, and categorizing the district into varying levels of susceptibility, ranging from low to high hazard zones. This will provide a comprehensive tool for local authorities and stakeholders to implement effective disaster management and mitigation strategies. The map will classify the region into zones of low, medium, high, and very high susceptibility, providing critical information for local authorities and stakeholders to implement disaster risk reduction strategies. Additionally, this study aims to fill a significant research gap in the region by applying these techniques to an area that has not been previously investigated. Landslides pose a significant threat to the Daramalo district, but the lack of detailed landslide susceptibility mapping hinders effective mitigation efforts. Without accurate and up-to-date information on landslide-prone areas, local governments and communities are left vulnerable to disaster. This study addresses this gap by employing cutting-edge geospatial techniques and statistical methods to identify landslide risk zones, providing valuable insights for future research and policy development in the region. This study is the first to apply geospatial Frequency Ratio methods to the Daramalo district, making it a novel contribution to the body of knowledge on landslide susceptibility mapping in Ethiopia. By integrating advanced mapping techniques with local data, the study not only enhances the understanding of landslide risks in the Gamo Highlands but also offers a replicable methodology for other regions facing similar challenges. 2. Materials and Method 2.1. Description of the study area Daramalo is one of the districts in Southern Ethiopia, located within the Gamo Zone. It is bordered to the southeast by Bonke, to the southwest by Kamba, to the west by Zala, to the north by Kucha, and the east by Dita Woreda. Geographically, Daramalo is situated between 37°17' E and 37°27'30'' E longitudes and 6°15'50'' N and 6°25'50'' N latitudes (Fig. 1 ). The woreda is approximately 420 kilometers from Ethiopia's capital, Addis Ababa, covering a total area of 250 square kilometers. Daramalo is accessible via an asphalt road from Arba Minch city to Wolaita Sodo, and from Sodo Kucha to Wacha, the road continues as gravel. The area is characterized by significant topographical variation and experiences a bimodal rainfall pattern. The average annual rainfall is approximately 224.9 mm, with the highest monthly average of 129.4 mm recorded in April. The region receives its peak rainfall between March and July, while the period from October to January sees comparatively low rainfall. 2.2. Data and method 2.2.1. Data Data relevant to this study has been collected, including a Digital Elevation Model (DEM) with a resolution of 12.5 × 12.5 meters, geological reports and maps, as well as Sentinel-2A ( https://scihub.copernicus.eu ) satellite images. These datasets were obtained from multiple reputable sources, including the Alaska Satellite Facility ( https://www.asf.alaska.edu ), the United States Geological Survey (USGS) ( https://www.usgs.gov ), the Geological Survey of Ethiopia (GSE) ( http://www.gse.gov.et ), fieldwork, and Google Earth imagery ( https://earth.google.com ). The combination of these diverse data sources ensures a comprehensive understanding of the study areas' geological and topographical features, which is essential for effective landslide susceptibility analysis. The Digital Elevation Model (DEM) provides crucial information on the topography, while geological reports and maps offer insights into the subsurface conditions that may influence landslide occurrences. Sentinel-2A images contribute valuable data on land cover and land use changes, drainage density, and lineament density, further enhancing the analysis of potential landslide triggers. By leveraging these datasets, the study aims to develop an accurate and detailed landslide distribution and susceptibility map, ultimately aiding in hazard assessment and management in the Gamo Highlands area. 2.2.2 Landslide inventory The prevalent types of landslides observed in the study area include rock topple, rock fall, rotational slide, debris fall, debris flow, earth fall, earth flow, rock slide, and complex movements. Occurrences of rock topple, rock fall, and rock slide are primarily concentrated in columnar basalt and vesicular-aphanitic basalt intercalations, while rotational slides, debris falls, debris flows, earth falls, and earth flows are predominantly found in superficial deposits. To provide input data for the susceptibility model and to validate the final susceptibility map, a landslide inventory map was created through time-lapse interpretation of Google Earth images and a comprehensive field survey. Before conducting fieldwork, we developed an initial landslide inventory using Google Earth. This was subsequently refined through detailed field observations, which validated and corrected any misinterpreted landslide locations. All identified landslides were digitized in Google Earth, exported in KML format, and then imported and converted into shapefiles within a separate ArcGIS 10.8 workspace. The 32 landslide inventory data shapefile was randomly divided into 70% training data and 30% validation data sets. The training data (70%) was utilized for developing the landslide susceptibility model, while the validation data (30%) was employed to assess the accuracy of the susceptibility maps (Fig. 2 ). In conjunction with the inventory map preparation, various types of landslide imagery were documented both in the field and through Google Earth. 2.2.3. Conditioning factor thematic layer preparation a. Slope gradient : Slope gradient significantly impacts slope stability by enhancing the gravitational forces acting on earth materials [ 12 ], [ 26 ]. Mostly, steeper slopes are more susceptible to instability compared to gentler slopes [ 27 ]. As a result, slope gradient is one of the most important and frequently utilized factors in landslide susceptibility mapping [ 10 ], [ 16 ], [ 28 ]. In this study, the slope map for the area was generated using a Digital Elevation Model (DEM) with a resolution of 12.5 m × 12.5 m. The slope map was then reclassified into five distinct classes based on slope angle and steepness, as illustrated in Fig. 3 a. b. Elevation : indirectly affects the occurrence of landslides by influencing various related factors, including rainfall patterns, hydration response rates, weathering depth and intensity, humidity fluctuations, erosion processes, and vegetation cover [ 16 ], [ 29 ], [ 30 ]. Commonly, weathering and erosion tend to be more pronounced at higher elevations. In this study, the elevation factor was extracted using the 12.5 m × 12.5 m Digital Elevation Model (DEM) and classified into five distinct classes 1130m – 1474m, 1474m – 1782m, 1782m – 2094m, 2094m – 2494m, and 2494m – 3100m, as depicted in Fig. 4 c. c. Slope aspect : refers to the direction a slope faces, measured in degrees (0°–360°) starting from the North [ 31 ], [ 32 ]. It is influenced by the regional tectonic setup of a given area. The slope aspect has a significant impact on various environmental factors, including the exposure of terrain to sunlight, vegetation cover, rainfall (and its degree of saturation), evapotranspiration, wind direction, discontinuity conditions, and the formation of channels and gullies. These factors, in turn, affect the degree of weathering and erosion, potentially leading to slope instability. Similar to the slope angle, the slope aspect map for this study was extracted from the 12.5 m × 12.5 m Digital Elevation Model (DEM) in Arc GIS 10.8 of spatial analyst tools and classified into nine distinct categories: Flat, North, Northeast, East, South, Southeast, Southwest, West, and Northwest, as illustrated in Fig. 3 b. d. Profile curvature : indicates the rate of change in ground slope and plays a critical role in predicting landslides within the study area [ 7 ], [ 33 ]. A Digital Elevation Model (DEM) was utilized to generate a curvature map, which was subsequently classified into three primary categories. A negative curvature value denotes concave slopes, while a positive curvature value indicates convex slopes (Fig. 4 d). The spatial concentration of drainage is influenced by high convexity and concavity, which can lead to slope saturation and eventual failure. e. Drainage Density : Slope instability is significantly influenced by drainage through two main mechanisms. First, river water can erode the base of slopes along both sides of the drainage, thereby increasing the slope gradient. Second, the presence of water increases pore water pressure and the self-weight of the slope material, which can reduce the inherent cohesion of the material in contact with water on both sides of the drainage. Numerous scholars, [ 7 ], [ 18 ], [ 34 ], [ 35 ], emphasize the critical role of drainage in landslide evaluation due to its significant impact on the occurrence of landslides. In this study, a Digital Elevation Model (DEM) with a resolution of 12.5 m × 12.5 m was employed to construct a drainage map. Hydrology tools were utilized to develop the drainage network, and the line density tool was applied to extract this network, resulting in five distinct classes: 0–1 km2/km, 1–2 km2/km, 2–3 km2/km, 3–4km2/km, and 4–5 km2/km (Fig. 5 e). f. Lineament density : Lineaments are linear surface features that represent underlying geological structures [ 36 ]. These linear features are often associated with zones of weakness that exhibit high permeability, which can facilitate slope failure [ 37 ], [ 38 ]. In this study, lineament traces were digitized from existing geological maps and Digital Elevation Model (DEM) hill-shade imagery, supplemented by field surveys. The data were categorized into three distinct classes: 0–0.23 km2/km, 0.23–0.65 km2/km, and 0.65–0.93 km2/km (Fig. 5 c). This classification aids in assessing the influence of geological structures on slope stability and landslide susceptibility. g. Lithology Lithology is a crucial factor influencing landslide occurrence due to the distinct engineering geological behaviors exhibited by various lithological units. Variations in strength and permeability among these units lead to diverse landslides [ 3 ], [ 15 ], [ 16 ]. Therefore, understanding the lithological characteristics of a region is essential for assessing how these factors contribute to landslide hazards and for making scientifically sound conclusions about landslide conditions. In this study, "Lithology" is defined to encompass rock types, ensuring a comprehensive evaluation of how lithology and surface materials affect landslide occurrence. To create a slope material map for the study area, the geological map of Ethiopia (1:50,000 scale) was used to digitize manually using Arc GIS 10.8 tools. Subsequently modified through field surveys to enhance accuracy before being integrated into the lithological map. Three lithological units were identified: Basalt, rhyolite, and ignimbrite (Fig. 6 g). h. Land use land cover : land use is also one of the key factors responsible which was extracted from the USGS Landsat image in July 2023 and produced using supervised image classification in Arc GIS 10.8 image classification array. The LULC classes were categorized into five including water bodies, shrubland, agricultural land, forest, and vegetation cover (Fig. 6 h). 2.2.4. Ferquency ration techniques This bivariate statistical model functions by examining the spatial association that has been observed between each land- land-slide-related element and the landslides [ 1 ], [ 9 ], [ 39 ]. According to [ 2 ], [ 17 ], the frequency ratio is computed as the mathematical proportion between the percent of landslide pixels and the percent of total area pixels within each class of elements (Eq. 1.). \(\:FR=\frac{LSpix/\sum\:_{n=1}^{n}LSpix}{\frac{Cpix}{{\sum\:}_{n=1}^{n}Cpix}}\) *100.....................................................................Eq. 1 where FR = frequency ratio, LSpix = count of landslide pixels within a parameter class, n = 1 LSpix = total of all area’s landslide pixel, Cpix = amount of pixel in factor class, and n = 1 Cpix = sum of all pixels in the factor class. A significant and positive connection between the landslide causal factor class and landslides can be detected by a frequency ratio (FR) value greater than 1. On the other hand, a FR value of less than one implies a weaker relationship between the factor class and the likelihood of landslides [ 2 ], [ 20 ], [ 27 ]. The relative frequency, or range of probability values between 0 and 1, is required to normalize the FR value of each class in the modified frequency ratio approach. After multiplying the RF values by 100, class weights are assigned to each factor class [ 10 ], [ 21 ]. Once all FR values were calculated, they were assigned to the respective causative factor classes in a GIS environment. The landslide susceptibility index (LSI) raster was obtained by summing the FR values of the eight causative factors using Eq. (2). LSI = FR (slope) + FR (aspect) + FR (elevation) + FR (curvature) + FR (drainage density) + FR (lineament density) + FR (lithology) + FR (land use land cover) [ 10 ]. 3. Results and Discussions 3.1. GIS-based Landslide susceptibility using FR method The frequency ratio (FR) method is widely used in landslide susceptibility mapping to assess the relationship between landslide occurrences and various contributing factors such as slope, aspect, elevation, curvature, drainage density, land use/land cover (LULC), lithology, and lineaments. It involves calculating the ratio of landslide pixels to total area pixels within each factor class, allowing for the identification of key classes that contribute to landslide occurrences. This section discusses the findings based on FR analysis applied to the given dataset (Table 1 ). Slope angle is one of the most critical factors influencing landslide susceptibility. The results reveal (Table 1 ), that slopes between 15°-24° have the highest FR value (2.725), indicating a significant contribution to landslide occurrences. Slopes of this range are steep enough to encourage material failure due to gravity. In contrast, lower slopes (-7.7° to 7.7°) and higher slopes (24°-35°) show lower FR values (0.991 and 0.578, respectively), implying that extremely gentle and very steep slopes have a lower likelihood of landslide occurrence, aligning with previous studies by [ 28 ], [ 29 ]. Aspect influences landslide susceptibility by determining the amount of solar radiation a slope receives, which affects soil moisture. In this study, the southwest-facing slopes show the highest FR (2.952), suggesting a greater landslide occurrence on these slopes. This might be due to weaker vegetation cover or higher erosion rates on these slopes. Conversely, south-facing slopes exhibit a lower FR (0.524), indicating lesser susceptibility. The spatial distribution of solar radiation might also influence the findings, corroborating research by [ 5 ], [ 19 ]. Landslide susceptibility varies across different elevation ranges. The FR value for elevations between 1782 and 2094m is notably high (2.446), suggesting a strong correlation between landslide occurrence and FR values. These areas may experience higher moisture levels or human activities such as agriculture, which can destabilize slopes. Higher elevations (2094 m − 3100 m) have lower FR values (0.339 to 0.66), implying that landslides are less likely to occur at such altitudes, possibly due to stable geological formations. These trends are consistent with previous findings by [ 34 ], [ 40 ]. Curvature influences water runoff and accumulation, which can trigger landslides. Flat areas exhibit the highest FR (5.332), indicating that landslides are most frequent in these regions, likely due to the accumulation of water and soil. Concave areas also show a relatively high FR (2.238), as these areas tend to retain moisture, increasing the risk of soil saturation and slope failure. Convex areas, with an FR of 0.312, are less susceptible, likely due to better drainage. Similar results have been noted by [ 41 ] in their study on the role of topography in landslides Drainage density impacts the amount of surface runoff and erosion. Areas with drainage density between 2–3 have an FR of 3.661, suggesting higher susceptibility to landslides. The concentration of water in these areas likely contributes to slope instability. Low drainage density (1–2) has a significantly lower FR of 0.514, implying reduced landslide occurrences due to lesser surface water concentration [ 2 ], [ 31 ]. Different land cover types exhibit varying FR values, with agricultural land showing a moderate susceptibility to landslides (FR = 0.872). Forested areas, on the other hand, exhibit a low FR of 0.453, highlighting their role in stabilizing slopes through root systems [ 20 ]. Shrubland shows a relatively higher FR of 1.436, indicating a potential risk due to less vegetation cover and increased surface runoff. The lithological units play a crucial role in slope stability. The basaltic formations exhibit an FR of 1.940, indicating that this lithology is highly susceptible to landslides due to its fractured nature, which allows water infiltration and promotes slope failure [ 8 ], [ 36 ]. Ignimbrites have a lower FR (0.627), signifying lower landslide potential, while rhyolites show moderate susceptibility with an FR of 0.523. Lineaments represent zones of structural weakness where landslides are more likely to occur. The FR value for areas with a lineament density between 0.23 and 0.65 is 1.45, indicating higher landslide susceptibility. This suggests that tectonic and geological structures in these areas create weaknesses that are exacerbated by external factors such as rainfall and slope gradient [ 38 ], [ 39 ], [ 42 ]. Low-density areas (0-0.23) exhibit reduced susceptibility, with an FR of 0.71. The results from the FR analysis highlight that certain factors, such as slope angle (15–24°), southwest aspect, mid-elevation (1782-2094m), and basaltic lithology, significantly contribute to higher landslide susceptibility. Understanding these factors is critical for developing hazard mitigation strategies in landslide-prone areas. The use of FR provides valuable insights into the spatial distribution of landslides, which can be integrated into land-use planning and risk management [ 4 ], [ 27 ], [ 43 ]. Future studies should consider incorporating more dynamic factors such as rainfall intensity and human activities to improve the accuracy of landslide susceptibility models. Table 1 Calculation result of FR for all factor classes Data layer Class LSpix LSpix(a) Cpix(a) Cpix(b) FR Slope angle 0 -7.7 6796 16200 40115 41100 0.991 7.7–15 8476 15100 40115 41100 0.325 15–24 7157 6200 40115 41100 2.725 24–35 5153 3400 40115 41100 0.578 35–73 2533 930 40115 41100 1.430 Aspect Flat(-1-35) 4466 56700 70700 215100 0.562 North 4906 46800 70700 215100 0.748 Northeast 4188 44100 70700 215100 0.678 East 3924 27900 70700 215100 1.004 South 3499 9900 70700 215100 0.524 Southeast 3088 1800 70700 215100 1.25 Southwest 1992 3600 70700 215100 2.952 West 1103 4500 70700 215100 1.750 Northwest 2949 19800 70700 215100 1.063 Elevation 1767-1967m 1493 3800 42128 78100 0.663 1130–1474 7394 5100 42128 78100 0.446 1474–1782 11283 35100 42128 78100 0.542 1782–2094 7971 36000 42128 78100 2.373 2094–2494 1974 9800 42128 78100 0.339 2494–3100 1862 7820 41230 78100 0.66 Curvature Flat 1493 1800 30500 193610 5.332 Convex 7394 15320 30500 193610 0.312 Concave 11283 32400 30500 193610 2.238 Drainage density 0–1 7999 13500 30035 134100 2.645 1–2 6323 54900 30035 134100 0.514 2–3 7378 9000 30035 134100 3.661 3–4 6036 49500 30035 134100 0.544 4–5 2299 7200 30035 134100 1.425 LULC Agricalutural land 2770 91800 29996 867150 0.872 Water body 3871 25740 29996 867150 0.434 Shrub land 4172 27630 29996 867150 1.436 Forst 8510 16920 29996 867150 0.453 Lithology Vegetation cover 6420 14930 26670 856120 1.31 Rhyolite 9897 2350 13781 49870 0.523 Ignimbrite 12360 4000 13781 49870 0.627 Basalt 4280 3300 13781 49870 1.940 Lineament 0–0.23 3450 2250 12400 47800 0.71 0.23–0.65 3190 1890 11000 43001 1.45 0.65–0.97 1170 23001 15300 2160 0.82 3.2. Landslide susceptibility Validation 3.2.1. Landslide susceptibility mapping (LSM) The landslide vulnerability map of the study area was classified into three distinct susceptibility zones based on the probability of landslide occurrences (Fig. 8 ). The analysis revealed that the very high susceptibility zone constitutes 44% of the total area, covering approximately 110 km². The moderate susceptibility zone occupies 36.7%, or about 92 km², while the low susceptibility zone accounts for 19.2%, covering 48 km² (Fig. 9 ). These results demonstrate significant spatial variation in landslide susceptibility, with the highest risk concentrated in certain areas. A closer examination of the spatial distribution of landslide-prone areas highlights that landslide susceptibility is particularly high along riverbanks and roadsides throughout the river basin (Fig. 7 & 2 ). The steep slopes, coupled with the proximity to hydrological networks and transportation infrastructure, make these areas more susceptible to landslide occurrences. Similar studies in other regions have reported comparable findings, indicating that riverbanks and roadsides often exhibit heightened landslide risks due to the combination of natural and anthropogenic factors [ 29 ], [ 44 ], [ 45 ]. The high susceptibility along rivers can be attributed to ongoing erosion processes and the saturation of soils during heavy rainfall events, which increase the likelihood of slope failures. Roads and their associated cut slopes, on the other hand, often disrupt the natural stability of slopes, further increasing landslide susceptibility. These observations align with the findings of previous research, which underscores the role of human activities, such as road construction, in exacerbating landslide risk in vulnerable terrains [ 6 ], [ 8 ], [ 27 ]. 3.2.2. Landslide Susceptibility Validation The Receiver Operating Characteristic (ROC) curve is widely recognized as an effective tool for assessing the efficacy of models in probabilistic diagnosis and prediction studies [ 22 ], [ 33 ]. The area under the ROC curve (AUC), which reflects the model's overall quality and accuracy, is generated by plotting the cumulative percentage of landslides (sensitivity) against the cumulative percentage of the landslide susceptibility index (LSI) in descending order (1-specificity) [ 25 ], [ 26 ]. The AUC value ranges from 0.5 to 1[ 2 ]. Specifically, AUC values of 0.9–1.0, 0.8–0.9, 0.7–0.8, and 0.5–0.6 represent excellent, very good, good, average, and fair model performance, respectively. Conversely, an AUC value equal to or less than 0.5 indicates poor model performance [ 46 ], [ 47 ]. The results of this study show that the AUC value for the validation of landslide prediction is AUC = 0.8903 (89.03%), demonstrating that the model applied in this study performs at a very high level (Fig. 10 ). In this study, the AUC value for the validation of landslide prediction was calculated to be 0.8903, or 89.03%, indicating a very good performance (Fig. 10 ). This high AUC value confirms the reliability and accuracy of the applied model in producing a credible landslide susceptibility map. Based on these results, we can confidently conclude that the model's predictive capabilities are robust and sufficient for identifying landslide-prone areas. Several authors have commended the accuracy of the frequency ratio method for landslide susceptibility evaluations under the findings of this work [ 4 ], [ 16 ], [ 44 ], [ 48 ]. 3.3. Evaluation of triggering factors Interviews with residents revealed that rainfall is widely considered the primary triggering factor for landslides in the present study area. Out of 15 respondents, 9 were able to recall the specific season when the failures occurred, with 7 reporting that landslides took place during the summer season. Furthermore, 5 respondents noted that the landslides occurred during or immediately after periods of heavy rainfall. This local knowledge points to a strong link between rainfall and slope instability, particularly during the rainy season. In addition to heavy rainfall, several respondents highlighted observable hydrological processes that preceded landslides. In areas where large landslides were reported, residents observed surface water seepage from the upper slopes into the subsurface, the emergence of springs, and localized flooding before slope failure. These signs indicate that saturation of the soil, increased pore water pressure, and surface runoff contributed to the destabilization of slopes, eventually leading to landslides. This aligns with the well-established understanding that water infiltration into slopes significantly reduces shear strength and increases the likelihood of slope failure [ 46 ], [ 49 ]–[ 51 ]. The results of these interviews strongly indicate that rainfall is the primary trigger for most of the reported landslide failures in the region. This finding is further supported by regional climate statistics, which show that the area experienced its highest precipitation levels during the summer months, specifically in 2019 and 2020 when major slope failures were reported (Fig. 7). The alignment of peak rainfall periods with landslide events highlights the critical role of climatic conditions in landslide occurrences. Previous studies have similarly demonstrated that high precipitation levels, particularly during the rainy season, are strongly linked to an increase in landslide activity [ 4 ], [ 52 ], [ 53 ]. In addition to seasonal rainfall, the presence of weak geological discontinuity zones has been identified as a significant inducing factor for landslides in the study area. These geological features can exacerbate the effects of rainfall by reducing slope stability and facilitating failure. Previous research has highlighted the importance of geological factors in landslide susceptibility, suggesting that areas with fractured or poorly consolidated materials are particularly susceptible when subjected to heavy precipitation [ 33 ], [ 54 ], [ 55 ]. This finding further supports the need for comprehensive assessments that incorporate both climatic and geological factors to understand landslide hazards effectively 4. Conclusion and Recommendation The current study is conducted in the Daramalo district, Gamo Zone, Southern Ethiopia, an area characterized by active gully erosion and rugged, undulating topography, with elevations ranging from 1,130 to 3,100 meters. Landslides pose a significant challenge in this region. The primary objective of this study was to evaluate landslide hazards and utilize the frequency ratio model to generate a landslide susceptibility map. Through the analysis of satellite images and field surveys, a total of 32 landslides were identified, with 70% classified as training landslides and 30% as validation landslides. In assessing the factors contributing to landslides, eight key factors were considered: slope, aspect, elevation, curvature, drainage density, lithology, lineaments density, and land use/land cover. The causative factor maps were integrated into the training landslide inventory to determine the weight for each landslide parameter class (FR). These weights were calculated based on the relative anomaly of landslides within specific causal factor classes. Additionally, the prediction rate (PR) for each causative factor was computed by analyzing the frequency ratio (FR) patterns among the classes. This approach quantifies the relative contribution of each factor to landslide occurrences. The landslide susceptibility index (LSI) map for the study area was produced by summing the FR values of the weighted causative factors. The analysis indicated that the very high susceptibility zone constitutes 44% of the total area, covering approximately 110 km², while the moderate susceptibility zone occupies 36.7% (about 92 km²), and the low susceptibility zone accounts for 19.2% (48 km²). The accuracy of the final landslide susceptibility map, generated using the frequency ratio model, was validated through ROC curve analysis, yielding a score of 89.03%. These findings provide a foundation for the development of more integrated approaches, encouraging further scientific investigation into landslide mechanisms and mitigation strategies. The susceptibility map produced is highly valuable for decision-makers involved in regional land use planning, site selection, and landslide prevention and mitigation initiatives. To effectively address the landslide issues in the study area, authorities at the federal, regional, zonal, and district levels must implement decisive actions. Recommended remedial measures include reforestation efforts by planting trees in barren areas, constructing check dams, building gabions and retaining walls, and relocating communities from unstable slopes. A combination of these interventions may yield the most effective results in reducing landslide risks. While this study employed a GIS-based approach covering a large area, it did not consider the engineering geological properties of the rocks and soils. Therefore, comprehensive geotechnical and geophysical studies are essential to develop more complex and effective landslide mitigation strategies. Such research will provide deeper insights into the subsurface conditions and contribute to more accurate hazard assessments and mitigation planning. Declarations Acknowledgment We would like to express our sincere gratitude to Arba Minch University for funding this research grant. Their support has been instrumental in enabling us to conduct this study and contribute valuable insights into landslide susceptibility mapping in the Daramalo district of the Gamo Zone, Southern Ethiopia. We also appreciate the contributions of all individuals and institutions that assisted in the data collection and analysis process, which enriched this research. Competing interest Regarding the research on landslide susceptibility mapping using the frequency ratio method in the Daramalo district of the Gamo zone, that is given in this publication, The authors declare to have no competing intentions. Data availability : The study’s supporting data, which include the landslide inventory, geological data, frequency ratio calculation results, validation tables, and other relevant datasets used for Landslide Susceptibility Mapping using the Frequency Ratio Method in the study area obtained from the corresponding author upon request. Certain datasets may require permission to access because location and privacy data are sensitive. References M. Khatun, A. T. M. S. Hossain, H. M. Sayem, M. Moniruzzaman, Z. Ahmed, and K. R. Rahaman, “Landslide Susceptibility Mapping Using Weighted-Overlay Approach in Rangamati, Bangladesh,” Earth Syst. Environ. , vol. 7, no. 1, pp. 223–235, 2023, doi: 10.1007/s41748-022-00312-2. M. Conforti and F. 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8","display":"","copyAsset":false,"role":"figure","size":580577,"visible":true,"origin":"","legend":"\u003cp\u003eThe landslide susceptibility map of the study uses frequency ratio techniques\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5154634/v1/ed531205a2f3c48978ebb2d2.png"},{"id":71559488,"identity":"604ab02d-33d8-483d-af1a-3b3bd5bf6bc0","added_by":"auto","created_at":"2024-12-16 16:45:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":10717,"visible":true,"origin":"","legend":"\u003cp\u003eLandslide susceptibility of study area\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5154634/v1/f8091fce155eae5c041425e8.png"},{"id":71559484,"identity":"3d4eb3b8-9794-430e-aedb-7096325ede78","added_by":"auto","created_at":"2024-12-16 16:45:35","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":18962,"visible":true,"origin":"","legend":"\u003cp\u003eROC validation of the FR model of the study area\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-5154634/v1/00301c907c16188f0777707a.png"},{"id":81427868,"identity":"2c6e7b80-7b7c-469d-a0c4-5332842ceb17","added_by":"auto","created_at":"2025-04-26 08:08:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7149784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5154634/v1/3ce1cd77-a75b-4751-b166-c780c60968d6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of Geospatial and Frequency Ratio Techniques in Landslide Susceptibility Mapping: Case Study of Daramalo District, Ethiopia","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eLandslides are among the most destructive natural hazards, particularly in mountainous regions, causing significant loss of life, damage to infrastructure, and environmental degradation globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These complex natural phenomena are often triggered by natural factors such as rainfall, snowmelt, or earthquakes, but they can also result from human activities, including improper road construction and deforestation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. To mitigate the impacts of landslides, governments, and research institutions have increasingly focused on landslide susceptibility mapping, which identifies areas at risk of landslide events. By doing so, they aim to inform disaster prevention strategies and reduce potential damage [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEthiopia, with its vast highland regions and variable climate, is highly susceptible to landslides, particularly during the rainy season when the annual precipitation exceeds 1200 mm in many areas [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Approximately 60% of the Ethiopian population lives in these highlands, making landslide events a significant threat to both lives and livelihoods. Historical records show instability in the superficial materials and bedrock in several parts of the northern, southern, and western Ethiopian Plateau [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite this, landslide hazard susceptibility mapping remains limited in many regions, including the Daramalo district of the Gamo Highlands, where this study is focused.\u003c/p\u003e \u003cp\u003eSeveral techniques have been developed to identify and map landslide susceptibility zones, each with its advantages and limitations. The multi-influencing factor (MIF) approach integrates various factors like slope, lithology, and land use to predict landslide-prone areas. This method, while comprehensive, requires a detailed dataset, which can be challenging to obtain in certain regions [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Weight of Evidence (WoE) is another widely used statistical method that evaluates the relationship between landslide occurrences and conditioning factors, providing a probabilistic measure of susceptibility [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. AHP (Analytic Hierarchy Process) relies on expert judgment to assign weights to different factors, which can introduce subjectivity but is effective in areas where empirical data is limited [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Frequency Ratio (FR) method, often paired with geospatial analysis, calculates the ratio of landslide occurrences to the presence of specific conditioning factors, making it a robust method for large-scale mapping [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Logistic regression, on the other hand, models the probability of landslide events based on the combination of multiple factors, offering a statistical perspective on susceptibility mapping [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Remote sensing and GIS tools have also played a critical role in advancing landslide studies, allowing for the efficient analysis of large datasets and the production of high-resolution maps [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Each of these methods has been employed in various global contexts, contributing valuable insights into the prediction and prevention of landslides.\u003c/p\u003e \u003cp\u003eThe present study employs geospatial analysis and the Frequency Ratio method techniques for landslide susceptibility mapping in the Daramalo district, Gamo Highlands. These methods were selected for their ability to handle complex topography data and diverse conditioning factors, such as slope, lithology, and land use. The Frequency Ratio method, in particular, is well-suited for the region due to its capacity to incorporate historical landslide data and produce statistically reliable susceptibility maps [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Geospatial analysis, combined with GIS, enables the efficient management of large datasets, making it a powerful tool for regional-scale mapping in data-scarce areas like Daramalo.\u003c/p\u003e \u003cp\u003eThe Daramalo district, located in the Gamo Highlands of southern Ethiopia, is characterized by steep slopes and complex geological structures, making it particularly susceptible to landslides. Despite this, no comprehensive landslide hazard studies have been conducted in the area, and no previous research has applied the geospatial and Frequency Ratio methods for susceptibility mapping. The study area represents an appropriate location for applying these techniques for the first time due to the incorporation of a variety of topographical and geological conditions. The primary objective of this study is to develop a landslide susceptibility map for the Daramalo district using geospatial analysis and the Frequency Ratio method. To identify and evaluate the key factors contributing to landslide occurrences in the Daramalo district, including topography, slope, lithology, land use, and, assign relative weights to these factors using the Frequency Ratio method. Furthermore, the study aims to develop a landslide susceptibility map by integrating these weighted factors in a GIS environment, validating the map using known landslide locations, and categorizing the district into varying levels of susceptibility, ranging from low to high hazard zones. This will provide a comprehensive tool for local authorities and stakeholders to implement effective disaster management and mitigation strategies. The map will classify the region into zones of low, medium, high, and very high susceptibility, providing critical information for local authorities and stakeholders to implement disaster risk reduction strategies. Additionally, this study aims to fill a significant research gap in the region by applying these techniques to an area that has not been previously investigated.\u003c/p\u003e \u003cp\u003eLandslides pose a significant threat to the Daramalo district, but the lack of detailed landslide susceptibility mapping hinders effective mitigation efforts. Without accurate and up-to-date information on landslide-prone areas, local governments and communities are left vulnerable to disaster. This study addresses this gap by employing cutting-edge geospatial techniques and statistical methods to identify landslide risk zones, providing valuable insights for future research and policy development in the region. This study is the first to apply geospatial Frequency Ratio methods to the Daramalo district, making it a novel contribution to the body of knowledge on landslide susceptibility mapping in Ethiopia. By integrating advanced mapping techniques with local data, the study not only enhances the understanding of landslide risks in the Gamo Highlands but also offers a replicable methodology for other regions facing similar challenges.\u003c/p\u003e"},{"header":"2. Materials and Method","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Description of the study area\u003c/h2\u003e\n \u003cp\u003eDaramalo is one of the districts in Southern Ethiopia, located within the Gamo Zone. It is bordered to the southeast by Bonke, to the southwest by Kamba, to the west by Zala, to the north by Kucha, and the east by Dita Woreda. Geographically, Daramalo is situated between 37\u0026deg;17\u0026apos; E and 37\u0026deg;27\u0026apos;30\u0026apos;\u0026apos; E longitudes and 6\u0026deg;15\u0026apos;50\u0026apos;\u0026apos; N and 6\u0026deg;25\u0026apos;50\u0026apos;\u0026apos; N latitudes (Fig. \u003cspan\u003e1\u003c/span\u003e). The woreda is approximately 420 kilometers from Ethiopia\u0026apos;s capital, Addis Ababa, covering a total area of 250 square kilometers. Daramalo is accessible via an asphalt road from Arba Minch city to Wolaita Sodo, and from Sodo Kucha to Wacha, the road continues as gravel. The area is characterized by significant topographical variation and experiences a bimodal rainfall pattern. The average annual rainfall is approximately 224.9 mm, with the highest monthly average of 129.4 mm recorded in April. The region receives its peak rainfall between March and July, while the period from October to January sees comparatively low rainfall.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2. Data and method\u003c/h2\u003e\n \u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.2.1. Data\u003c/h2\u003e\n \u003cp\u003eData relevant to this study has been collected, including a Digital Elevation Model (DEM) with a resolution of 12.5 \u0026times; 12.5 meters, geological reports and maps, as well as Sentinel-2A (\u003cspan\u003e\u003cspan\u003ehttps://scihub.copernicus.eu\u003c/span\u003e\u003c/span\u003e) satellite images. These datasets were obtained from multiple reputable sources, including the Alaska Satellite Facility (\u003cspan\u003e\u003cspan\u003ehttps://www.asf.alaska.edu\u003c/span\u003e\u003c/span\u003e), the United States Geological Survey (USGS) (\u003cspan\u003e\u003cspan\u003ehttps://www.usgs.gov\u003c/span\u003e\u003c/span\u003e), the Geological Survey of Ethiopia (GSE) (\u003cspan\u003e\u003cspan\u003ehttp://www.gse.gov.et\u003c/span\u003e\u003c/span\u003e), fieldwork, and Google Earth imagery (\u003cspan\u003e\u003cspan\u003ehttps://earth.google.com\u003c/span\u003e\u003c/span\u003e). The combination of these diverse data sources ensures a comprehensive understanding of the study areas\u0026apos; geological and topographical features, which is essential for effective landslide susceptibility analysis. The Digital Elevation Model (DEM) provides crucial information on the topography, while geological reports and maps offer insights into the subsurface conditions that may influence landslide occurrences. Sentinel-2A images contribute valuable data on land cover and land use changes, drainage density, and lineament density, further enhancing the analysis of potential landslide triggers. By leveraging these datasets, the study aims to develop an accurate and detailed landslide distribution and susceptibility map, ultimately aiding in hazard assessment and management in the Gamo Highlands area.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.2.2 Landslide inventory\u003c/h2\u003e\n \u003cp\u003eThe prevalent types of landslides observed in the study area include rock topple, rock fall, rotational slide, debris fall, debris flow, earth fall, earth flow, rock slide, and complex movements. Occurrences of rock topple, rock fall, and rock slide are primarily concentrated in columnar basalt and vesicular-aphanitic basalt intercalations, while rotational slides, debris falls, debris flows, earth falls, and earth flows are predominantly found in superficial deposits. To provide input data for the susceptibility model and to validate the final susceptibility map, a landslide inventory map was created through time-lapse interpretation of Google Earth images and a comprehensive field survey.\u003c/p\u003e\n \u003cp\u003eBefore conducting fieldwork, we developed an initial landslide inventory using Google Earth. This was subsequently refined through detailed field observations, which validated and corrected any misinterpreted landslide locations. All identified landslides were digitized in Google Earth, exported in KML format, and then imported and converted into shapefiles within a separate ArcGIS 10.8 workspace. The 32 landslide inventory data shapefile was randomly divided into 70% training data and 30% validation data sets. The training data (70%) was utilized for developing the landslide susceptibility model, while the validation data (30%) was employed to assess the accuracy of the susceptibility maps (Fig. \u003cspan\u003e2\u003c/span\u003e). In conjunction with the inventory map preparation, various types of landslide imagery were documented both in the field and through Google Earth.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.2.3. Conditioning factor thematic layer preparation\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003ea. Slope gradient\u003c/strong\u003e: Slope gradient significantly impacts slope stability by enhancing the gravitational forces acting on earth materials [\u003cspan\u003e12\u003c/span\u003e], [\u003cspan\u003e26\u003c/span\u003e]. Mostly, steeper slopes are more susceptible to instability compared to gentler slopes [\u003cspan\u003e27\u003c/span\u003e]. As a result, slope gradient is one of the most important and frequently utilized factors in landslide susceptibility mapping [\u003cspan\u003e10\u003c/span\u003e], [\u003cspan\u003e16\u003c/span\u003e], [\u003cspan\u003e28\u003c/span\u003e]. In this study, the slope map for the area was generated using a Digital Elevation Model (DEM) with a resolution of 12.5 m \u0026times; 12.5 m. The slope map was then reclassified into five distinct classes based on slope angle and steepness, as illustrated in Fig. \u003cspan\u003e3\u003c/span\u003ea.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eb. Elevation\u003c/strong\u003e: indirectly affects the occurrence of landslides by influencing various related factors, including rainfall patterns, hydration response rates, weathering depth and intensity, humidity fluctuations, erosion processes, and vegetation cover [\u003cspan\u003e16\u003c/span\u003e], [\u003cspan\u003e29\u003c/span\u003e], [\u003cspan\u003e30\u003c/span\u003e]. Commonly, weathering and erosion tend to be more pronounced at higher elevations. In this study, the elevation factor was extracted using the 12.5 m \u0026times; 12.5 m Digital Elevation Model (DEM) and classified into five distinct classes 1130m \u0026ndash; 1474m, 1474m \u0026ndash; 1782m, 1782m \u0026ndash; 2094m, 2094m \u0026ndash; 2494m, and 2494m \u0026ndash; 3100m, as depicted in Fig. \u003cspan\u003e4\u003c/span\u003ec.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u003cstrong\u003ec. Slope aspect\u003c/strong\u003e: refers to the direction a slope faces, measured in degrees (0\u0026deg;\u0026ndash;360\u0026deg;) starting from the North [\u003cspan\u003e31\u003c/span\u003e], [\u003cspan\u003e32\u003c/span\u003e]. It is influenced by the regional tectonic setup of a given area. The slope aspect has a significant impact on various environmental factors, including the exposure of terrain to sunlight, vegetation cover, rainfall (and its degree of saturation), evapotranspiration, wind direction, discontinuity conditions, and the formation of channels and gullies. These factors, in turn, affect the degree of weathering and erosion, potentially leading to slope instability. Similar to the slope angle, the slope aspect map for this study was extracted from the 12.5 m \u0026times; 12.5 m Digital Elevation Model (DEM) in Arc GIS 10.8 of spatial analyst tools and classified into nine distinct categories: Flat, North, Northeast, East, South, Southeast, Southwest, West, and Northwest, as illustrated in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eb.\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e\u003cstrong\u003ed. Profile curvature\u003c/strong\u003e: indicates the rate of change in ground slope and plays a critical role in predicting landslides within the study area [\u003cspan\u003e7\u003c/span\u003e], [\u003cspan\u003e33\u003c/span\u003e]. A Digital Elevation Model (DEM) was utilized to generate a curvature map, which was subsequently classified into three primary categories. A negative curvature value denotes concave slopes, while a positive curvature value indicates convex slopes (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003ed). The spatial concentration of drainage is influenced by high convexity and concavity, which can lead to slope saturation and eventual failure.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ee. Drainage Density\u003c/strong\u003e: Slope instability is significantly influenced by drainage through two main mechanisms. First, river water can erode the base of slopes along both sides of the drainage, thereby increasing the slope gradient. Second, the presence of water increases pore water pressure and the self-weight of the slope material, which can reduce the inherent cohesion of the material in contact with water on both sides of the drainage. Numerous scholars, [\u003cspan\u003e7\u003c/span\u003e], [\u003cspan\u003e18\u003c/span\u003e], [\u003cspan\u003e34\u003c/span\u003e], [\u003cspan\u003e35\u003c/span\u003e], emphasize the critical role of drainage in landslide evaluation due to its significant impact on the occurrence of landslides. In this study, a Digital Elevation Model (DEM) with a resolution of 12.5 m \u0026times; 12.5 m was employed to construct a drainage map. Hydrology tools were utilized to develop the drainage network, and the line density tool was applied to extract this network, resulting in five distinct classes: 0\u0026ndash;1 km2/km, 1\u0026ndash;2 km2/km, 2\u0026ndash;3 km2/km, 3\u0026ndash;4km2/km, and 4\u0026ndash;5 km2/km (Fig. \u003cspan\u003e5\u003c/span\u003ee).\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u003cstrong\u003ef. Lineament density\u003c/strong\u003e: Lineaments are linear surface features that represent underlying geological structures [\u003cspan\u003e36\u003c/span\u003e]. These linear features are often associated with zones of weakness that exhibit high permeability, which can facilitate slope failure [\u003cspan\u003e37\u003c/span\u003e], [\u003cspan\u003e38\u003c/span\u003e]. In this study, lineament traces were digitized from existing geological maps and Digital Elevation Model (DEM) hill-shade imagery, supplemented by field surveys. The data were categorized into three distinct classes: 0\u0026ndash;0.23 km2/km, 0.23\u0026ndash;0.65 km2/km, and 0.65\u0026ndash;0.93 km2/km (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003ec). This classification aids in assessing the influence of geological structures on slope stability and landslide susceptibility.\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e\u003cstrong\u003eg. Lithology\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eLithology is a crucial factor influencing landslide occurrence due to the distinct engineering geological behaviors exhibited by various lithological units. Variations in strength and permeability among these units lead to diverse landslides [\u003cspan\u003e3\u003c/span\u003e], [\u003cspan\u003e15\u003c/span\u003e], [\u003cspan\u003e16\u003c/span\u003e]. Therefore, understanding the lithological characteristics of a region is essential for assessing how these factors contribute to landslide hazards and for making scientifically sound conclusions about landslide conditions. In this study, \u0026quot;Lithology\u0026quot; is defined to encompass rock types, ensuring a comprehensive evaluation of how lithology and surface materials affect landslide occurrence.\u003c/p\u003e\n \u003cp\u003eTo create a slope material map for the study area, the geological map of Ethiopia (1:50,000 scale) was used to digitize manually using Arc GIS 10.8 tools. Subsequently modified through field surveys to enhance accuracy before being integrated into the lithological map. Three lithological units were identified: Basalt, rhyolite, and ignimbrite (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eg).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eh. Land use land cover\u003c/strong\u003e: land use is also one of the key factors responsible which was extracted from the USGS Landsat image in July 2023 and produced using supervised image classification in Arc GIS 10.8 image classification array. The LULC classes were categorized into five including water bodies, shrubland, agricultural land, forest, and vegetation cover (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eh).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.2.4. Ferquency ration techniques\u003c/h2\u003e\n \u003cp\u003eThis bivariate statistical model functions by examining the spatial association that has been observed between each land- land-slide-related element and the landslides [\u003cspan\u003e1\u003c/span\u003e], [\u003cspan\u003e9\u003c/span\u003e], [\u003cspan\u003e39\u003c/span\u003e]. According to [\u003cspan\u003e2\u003c/span\u003e], [\u003cspan\u003e17\u003c/span\u003e], the frequency ratio is computed as the mathematical proportion between the percent of landslide pixels and the percent of total area pixels within each class of elements (Eq.\u0026nbsp;1.).\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\:FR=\\frac{LSpix/\\sum\\:_{n=1}^{n}LSpix}{\\frac{Cpix}{{\\sum\\:}_{n=1}^{n}Cpix}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e*100.....................................................................Eq.\u0026nbsp;1\u003c/p\u003e\n \u003cp\u003ewhere FR\u0026thinsp;=\u0026thinsp;frequency ratio, LSpix\u0026thinsp;=\u0026thinsp;count of landslide pixels within a parameter class, n\u0026thinsp;=\u0026thinsp;1 LSpix\u0026thinsp;=\u0026thinsp;total of all area\u0026rsquo;s landslide pixel, Cpix\u0026thinsp;=\u0026thinsp;amount of pixel in factor class, and n\u0026thinsp;=\u0026thinsp;1 Cpix\u0026thinsp;=\u0026thinsp;sum of all pixels in the factor class. A significant and positive connection between the landslide causal factor class and landslides can be detected by a frequency ratio (FR) value greater than 1. On the other hand, a FR value of less than one implies a weaker relationship between the factor class and the likelihood of landslides [\u003cspan\u003e2\u003c/span\u003e], [\u003cspan\u003e20\u003c/span\u003e], [\u003cspan\u003e27\u003c/span\u003e]. The relative frequency, or range of probability values between 0 and 1, is required to normalize the FR value of each class in the modified frequency ratio approach. After multiplying the RF values by 100, class weights are assigned to each factor class [\u003cspan\u003e10\u003c/span\u003e], [\u003cspan\u003e21\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eOnce all FR values were calculated, they were assigned to the respective causative factor classes in a GIS environment. The landslide susceptibility index (LSI) raster was obtained by summing the FR values of the eight causative factors using Eq.\u0026nbsp;(2).\u003c/p\u003e\n \u003cp\u003eLSI\u0026thinsp;=\u0026thinsp;FR (slope)\u0026thinsp;+\u0026thinsp;FR (aspect)\u0026thinsp;+\u0026thinsp;FR (elevation)\u0026thinsp;+\u0026thinsp;FR (curvature)\u0026thinsp;+\u0026thinsp;FR (drainage density)\u0026thinsp;+\u0026thinsp;FR (lineament density)\u0026thinsp;+\u0026thinsp;FR (lithology)\u0026thinsp;+\u0026thinsp;FR (land use land cover) [\u003cspan\u003e10\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results and Discussions","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. GIS-based Landslide susceptibility using FR method\u003c/h2\u003e \u003cp\u003eThe frequency ratio (FR) method is widely used in landslide susceptibility mapping to assess the relationship between landslide occurrences and various contributing factors such as slope, aspect, elevation, curvature, drainage density, land use/land cover (LULC), lithology, and lineaments. It involves calculating the ratio of landslide pixels to total area pixels within each factor class, allowing for the identification of key classes that contribute to landslide occurrences. This section discusses the findings based on FR analysis applied to the given dataset (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSlope angle is one of the most critical factors influencing landslide susceptibility. The results reveal (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), that slopes between 15\u0026deg;-24\u0026deg; have the highest FR value (2.725), indicating a significant contribution to landslide occurrences. Slopes of this range are steep enough to encourage material failure due to gravity. In contrast, lower slopes (-7.7\u0026deg; to 7.7\u0026deg;) and higher slopes (24\u0026deg;-35\u0026deg;) show lower FR values (0.991 and 0.578, respectively), implying that extremely gentle and very steep slopes have a lower likelihood of landslide occurrence, aligning with previous studies by [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Aspect influences landslide susceptibility by determining the amount of solar radiation a slope receives, which affects soil moisture. In this study, the southwest-facing slopes show the highest FR (2.952), suggesting a greater landslide occurrence on these slopes. This might be due to weaker vegetation cover or higher erosion rates on these slopes. Conversely, south-facing slopes exhibit a lower FR (0.524), indicating lesser susceptibility. The spatial distribution of solar radiation might also influence the findings, corroborating research by [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLandslide susceptibility varies across different elevation ranges. The FR value for elevations between 1782 and 2094m is notably high (2.446), suggesting a strong correlation between landslide occurrence and FR values. These areas may experience higher moisture levels or human activities such as agriculture, which can destabilize slopes. Higher elevations (2094 m \u0026minus;\u0026thinsp;3100 m) have lower FR values (0.339 to 0.66), implying that landslides are less likely to occur at such altitudes, possibly due to stable geological formations. These trends are consistent with previous findings by [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Curvature influences water runoff and accumulation, which can trigger landslides. Flat areas exhibit the highest FR (5.332), indicating that landslides are most frequent in these regions, likely due to the accumulation of water and soil. Concave areas also show a relatively high FR (2.238), as these areas tend to retain moisture, increasing the risk of soil saturation and slope failure. Convex areas, with an FR of 0.312, are less susceptible, likely due to better drainage. Similar results have been noted by [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] in their study on the role of topography in landslides\u003c/p\u003e \u003cp\u003eDrainage density impacts the amount of surface runoff and erosion. Areas with drainage density between 2\u0026ndash;3 have an FR of 3.661, suggesting higher susceptibility to landslides. The concentration of water in these areas likely contributes to slope instability. Low drainage density (1\u0026ndash;2) has a significantly lower FR of 0.514, implying reduced landslide occurrences due to lesser surface water concentration [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Different land cover types exhibit varying FR values, with agricultural land showing a moderate susceptibility to landslides (FR\u0026thinsp;=\u0026thinsp;0.872). Forested areas, on the other hand, exhibit a low FR of 0.453, highlighting their role in stabilizing slopes through root systems [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Shrubland shows a relatively higher FR of 1.436, indicating a potential risk due to less vegetation cover and increased surface runoff. The lithological units play a crucial role in slope stability. The basaltic formations exhibit an FR of 1.940, indicating that this lithology is highly susceptible to landslides due to its fractured nature, which allows water infiltration and promotes slope failure [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Ignimbrites have a lower FR (0.627), signifying lower landslide potential, while rhyolites show moderate susceptibility with an FR of 0.523. Lineaments represent zones of structural weakness where landslides are more likely to occur. The FR value for areas with a lineament density between 0.23 and 0.65 is 1.45, indicating higher landslide susceptibility. This suggests that tectonic and geological structures in these areas create weaknesses that are exacerbated by external factors such as rainfall and slope gradient [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Low-density areas (0-0.23) exhibit reduced susceptibility, with an FR of 0.71.\u003c/p\u003e \u003cp\u003eThe results from the FR analysis highlight that certain factors, such as slope angle (15\u0026ndash;24\u0026deg;), southwest aspect, mid-elevation (1782-2094m), and basaltic lithology, significantly contribute to higher landslide susceptibility. Understanding these factors is critical for developing hazard mitigation strategies in landslide-prone areas. The use of FR provides valuable insights into the spatial distribution of landslides, which can be integrated into land-use planning and risk management [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Future studies should consider incorporating more dynamic factors such as rainfall intensity and human activities to improve the accuracy of landslide susceptibility models.\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\u003eCalculation result of FR for all factor classes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData layer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLSpix\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLSpix(a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCpix(a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCpix(b)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSlope angle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 -7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.7\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlat(-1-35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoutheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1767-1967m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1130\u0026ndash;1474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1474\u0026ndash;1782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1782\u0026ndash;2094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2094\u0026ndash;2494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2494\u0026ndash;3100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCurvature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e193610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConvex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e193610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e193610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDrainage density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricalutural land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e867150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e867150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShrub land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e867150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e867150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLithology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e856120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhyolite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgnimbrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLineament\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u0026ndash;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82\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 \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Landslide susceptibility Validation\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Landslide susceptibility mapping (LSM)\u003c/h2\u003e \u003cp\u003eThe landslide vulnerability map of the study area was classified into three distinct susceptibility zones based on the probability of landslide occurrences (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The analysis revealed that the very high susceptibility zone constitutes 44% of the total area, covering approximately 110 km\u0026sup2;. The moderate susceptibility zone occupies 36.7%, or about 92 km\u0026sup2;, while the low susceptibility zone accounts for 19.2%, covering 48 km\u0026sup2; (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e). These results demonstrate significant spatial variation in landslide susceptibility, with the highest risk concentrated in certain areas.\u003c/p\u003e \u003cp\u003eA closer examination of the spatial distribution of landslide-prone areas highlights that landslide susceptibility is particularly high along riverbanks and roadsides throughout the river basin (Fig.\u0026nbsp;7 \u0026amp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The steep slopes, coupled with the proximity to hydrological networks and transportation infrastructure, make these areas more susceptible to landslide occurrences. Similar studies in other regions have reported comparable findings, indicating that riverbanks and roadsides often exhibit heightened landslide risks due to the combination of natural and anthropogenic factors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The high susceptibility along rivers can be attributed to ongoing erosion processes and the saturation of soils during heavy rainfall events, which increase the likelihood of slope failures. Roads and their associated cut slopes, on the other hand, often disrupt the natural stability of slopes, further increasing landslide susceptibility. These observations align with the findings of previous research, which underscores the role of human activities, such as road construction, in exacerbating landslide risk in vulnerable terrains [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Landslide Susceptibility Validation\u003c/h2\u003e \u003cp\u003eThe Receiver Operating Characteristic (ROC) curve is widely recognized as an effective tool for assessing the efficacy of models in probabilistic diagnosis and prediction studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The area under the ROC curve (AUC), which reflects the model's overall quality and accuracy, is generated by plotting the cumulative percentage of landslides (sensitivity) against the cumulative percentage of the landslide susceptibility index (LSI) in descending order (1-specificity) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The AUC value ranges from 0.5 to 1[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Specifically, AUC values of 0.9\u0026ndash;1.0, 0.8\u0026ndash;0.9, 0.7\u0026ndash;0.8, and 0.5\u0026ndash;0.6 represent excellent, very good, good, average, and fair model performance, respectively. Conversely, an AUC value equal to or less than 0.5 indicates poor model performance [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The results of this study show that the AUC value for the validation of landslide prediction is AUC\u0026thinsp;=\u0026thinsp;0.8903 (89.03%), demonstrating that the model applied in this study performs at a very high level (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In this study, the AUC value for the validation of landslide prediction was calculated to be 0.8903, or 89.03%, indicating a very good performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e). This high AUC value confirms the reliability and accuracy of the applied model in producing a credible landslide susceptibility map. Based on these results, we can confidently conclude that the model's predictive capabilities are robust and sufficient for identifying landslide-prone areas. Several authors have commended the accuracy of the frequency ratio method for landslide susceptibility evaluations under the findings of this work [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Evaluation of triggering factors\u003c/h2\u003e \u003cp\u003eInterviews with residents revealed that rainfall is widely considered the primary triggering factor for landslides in the present study area. Out of 15 respondents, 9 were able to recall the specific season when the failures occurred, with 7 reporting that landslides took place during the summer season. Furthermore, 5 respondents noted that the landslides occurred during or immediately after periods of heavy rainfall. This local knowledge points to a strong link between rainfall and slope instability, particularly during the rainy season. In addition to heavy rainfall, several respondents highlighted observable hydrological processes that preceded landslides. In areas where large landslides were reported, residents observed surface water seepage from the upper slopes into the subsurface, the emergence of springs, and localized flooding before slope failure. These signs indicate that saturation of the soil, increased pore water pressure, and surface runoff contributed to the destabilization of slopes, eventually leading to landslides. This aligns with the well-established understanding that water infiltration into slopes significantly reduces shear strength and increases the likelihood of slope failure [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The results of these interviews strongly indicate that rainfall is the primary trigger for most of the reported landslide failures in the region. This finding is further supported by regional climate statistics, which show that the area experienced its highest precipitation levels during the summer months, specifically in 2019 and 2020 when major slope failures were reported (Fig.\u0026nbsp;7). The alignment of peak rainfall periods with landslide events highlights the critical role of climatic conditions in landslide occurrences. Previous studies have similarly demonstrated that high precipitation levels, particularly during the rainy season, are strongly linked to an increase in landslide activity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to seasonal rainfall, the presence of weak geological discontinuity zones has been identified as a significant inducing factor for landslides in the study area. These geological features can exacerbate the effects of rainfall by reducing slope stability and facilitating failure. Previous research has highlighted the importance of geological factors in landslide susceptibility, suggesting that areas with fractured or poorly consolidated materials are particularly susceptible when subjected to heavy precipitation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This finding further supports the need for comprehensive assessments that incorporate both climatic and geological factors to understand landslide hazards effectively\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion and Recommendation","content":"\u003cp\u003eThe current study is conducted in the Daramalo district, Gamo Zone, Southern Ethiopia, an area characterized by active gully erosion and rugged, undulating topography, with elevations ranging from 1,130 to 3,100 meters. Landslides pose a significant challenge in this region. The primary objective of this study was to evaluate landslide hazards and utilize the frequency ratio model to generate a landslide susceptibility map. Through the analysis of satellite images and field surveys, a total of 32 landslides were identified, with 70% classified as training landslides and 30% as validation landslides. In assessing the factors contributing to landslides, eight key factors were considered: slope, aspect, elevation, curvature, drainage density, lithology, lineaments density, and land use/land cover. The causative factor maps were integrated into the training landslide inventory to determine the weight for each landslide parameter class (FR). These weights were calculated based on the relative anomaly of landslides within specific causal factor classes. Additionally, the prediction rate (PR) for each causative factor was computed by analyzing the frequency ratio (FR) patterns among the classes. This approach quantifies the relative contribution of each factor to landslide occurrences. The landslide susceptibility index (LSI) map for the study area was produced by summing the FR values of the weighted causative factors.\u003c/p\u003e \u003cp\u003eThe analysis indicated that the very high susceptibility zone constitutes 44% of the total area, covering approximately 110 km\u0026sup2;, while the moderate susceptibility zone occupies 36.7% (about 92 km\u0026sup2;), and the low susceptibility zone accounts for 19.2% (48 km\u0026sup2;). The accuracy of the final landslide susceptibility map, generated using the frequency ratio model, was validated through ROC curve analysis, yielding a score of 89.03%. These findings provide a foundation for the development of more integrated approaches, encouraging further scientific investigation into landslide mechanisms and mitigation strategies. The susceptibility map produced is highly valuable for decision-makers involved in regional land use planning, site selection, and landslide prevention and mitigation initiatives. To effectively address the landslide issues in the study area, authorities at the federal, regional, zonal, and district levels must implement decisive actions. Recommended remedial measures include reforestation efforts by planting trees in barren areas, constructing check dams, building gabions and retaining walls, and relocating communities from unstable slopes. A combination of these interventions may yield the most effective results in reducing landslide risks. While this study employed a GIS-based approach covering a large area, it did not consider the engineering geological properties of the rocks and soils. Therefore, comprehensive geotechnical and geophysical studies are essential to develop more complex and effective landslide mitigation strategies. Such research will provide deeper insights into the subsurface conditions and contribute to more accurate hazard assessments and mitigation planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to Arba Minch University for funding this research grant. Their support has been instrumental in enabling us to conduct this study and contribute valuable insights into landslide susceptibility mapping in the Daramalo district of the Gamo Zone, Southern Ethiopia. We also appreciate the contributions of all individuals and institutions that assisted in the data collection and analysis process, which enriched this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the research on landslide susceptibility mapping using the frequency ratio method in the Daramalo district of the Gamo zone, that is given in this publication, The authors declare to have no competing intentions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: The study\u0026rsquo;s supporting data, which include the landslide inventory, geological data, frequency ratio calculation results, validation tables, and other relevant datasets used for Landslide Susceptibility Mapping using the Frequency Ratio Method in the study area obtained from the corresponding author upon request. Certain datasets may require permission to access because location and privacy data are sensitive.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM. Khatun, A. T. M. S. Hossain, H. M. Sayem, M. Moniruzzaman, Z. Ahmed, and K. R. Rahaman, \u0026ldquo;Landslide Susceptibility Mapping Using Weighted-Overlay Approach in Rangamati, Bangladesh,\u0026rdquo; \u003cem\u003eEarth Syst. Environ.\u003c/em\u003e, vol. 7, no. 1, pp. 223\u0026ndash;235, 2023, doi: 10.1007/s41748-022-00312-2.\u003c/li\u003e\n\u003cli\u003eM. Conforti and F. Ietto, \u0026ldquo;Modeling shallow landslide susceptibility and assessment of the relative importance of predisposing factors, through a gis‐based statistical analysis,\u0026rdquo; \u003cem\u003eGeosci.\u003c/em\u003e, vol. 11, no. 8, pp. 1\u0026ndash;28, 2021, doi: 10.3390/geosciences11080333.\u003c/li\u003e\n\u003cli\u003eT. Mersha and M. 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India\u003c/em\u003e, vol. 82, no. 4, pp. 351\u0026ndash;362, 2013, doi: 10.1007/s12594-013-0162-z.\u003c/li\u003e\n\u003cli\u003eB. Abebe, F. Dramis, G. Fubelli, M. Umer, and A. Asrat, \u0026ldquo;Landslides in the Ethiopian highlands and the Rift margins,\u0026rdquo; \u003cem\u003eJ. African Earth Sci.\u003c/em\u003e, vol. 56, no. 4\u0026ndash;5, pp. 131\u0026ndash;138, 2010, doi: 10.1016/j.jafrearsci.2009.06.006.\u003c/li\u003e\n\u003cli\u003eT. Ayenew and G. Barbieri, \u0026ldquo;Inventory of landslides and susceptibility mapping in the Dessie area, northern Ethiopia,\u0026rdquo; \u003cem\u003eEng. Geol.\u003c/em\u003e, vol. 77, no. 1\u0026ndash;2, pp. 1\u0026ndash;15, 2005, doi: 10.1016/j.enggeo.2004.07.002.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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, Frequency ratio model, Daramalo District, Gamo Zone","lastPublishedDoi":"10.21203/rs.3.rs-5154634/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5154634/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDaramalo district, located in the Gamo Zone of South Ethiopia, is one of the areas most affected by landslides. This study aims to assess the landslide susceptibility of the area and to develop a comprehensive landslide susceptibility map. To achieve this, a bivariate statistical frequency ratio model was employed. A detailed inventory of landslides was compiled through fieldwork and the interpretation of Google Earth imagery, identifying a total of 32 landslides. These were categorized into training landslides (70%) for model development and validation landslides (30%) for model evaluation. Eight causative factors slope, aspect, elevation, curvature profile, drainage density, lithology, lineament density, and land use/land cover (LULC) were integrated with the training landslide data to determine the frequency ratio values for each class of these factors. Relative frequency values were assigned to the appropriate factor classes, which were then summed using a raster calculator algorithm to produce the landslide susceptibility map. The final susceptibility map indicates that 44% (110 km\u0026sup2;) of the study area is classified as low susceptibility, 36.8% (92 km\u0026sup2;) as moderate susceptibility, and 19.2% (48 km\u0026sup2;) as high susceptibility. This suggests that approximately 20% of the area is at significant hazard of landslides, while about 80% has relatively low to moderate susceptibility to this natural hazard. The performance of the frequency ratio model was validated using the receiver operating characteristic (ROC) curve, achieving a notable success prediction rate of 89.03%. Overall, the model demonstrated strong accuracy. The resulting map is anticipated to be a valuable resource for land use planning, site selection, and the formulation of effective landslide prevention and mitigation strategies.\u003c/p\u003e","manuscriptTitle":"Application of Geospatial and Frequency Ratio Techniques in Landslide Susceptibility Mapping: Case Study of Daramalo District, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 16:45:30","doi":"10.21203/rs.3.rs-5154634/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":"4f086504-afbe-467b-8486-8449d3d1147e","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-26T08:08:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 16:45:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5154634","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5154634","identity":"rs-5154634","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}