{"paper_id":"0bb47bef-62c5-4274-8311-7bd4e5ec72bb","body_text":"Landslide Susceptibility Assessment in Greece: work in regional and national scale | 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 Landslide Susceptibility Assessment in Greece: work in regional and national scale Katerina Kavoura, Spanou Natalia, Apostolidis Emmanuel, Kokkali Panagiota, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4838383/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The current work figures out an approach for estimating landslide susceptibility in Greece. This is the first study to undertake the updated official National Geodatabase of Landslides, which encompasses the entire country for the purpose of analyzing landslide susceptibility. This research acknowledges the critical role that scale plays in landslide susceptibility modelling. From this perspective, regional scale analyses conducted in selected areas along Greece, to gain a deeper understanding of the challenges encountered by the process in ensuring the results of national scale assessment after that. The methodology followed in the current work is the comparison and evaluation of the landslide susceptibility that derives from a statistical method (quantitative analysis) and an expert-based method (qualitative). Afterwords, this research focuses on the evaluation of the results in any scale and suggests a framework for working on landslides susceptibility assessment in Greece. Landslide inventory FR AHP ROC curves landslide hazard multi-scales predisposing factors national database of landslides Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Landslide susceptibility assessment is a commonly used and essential procedure for indicating and classifying the spatial distribution of landslide prone areas. An amount of literature has been published on this topic over the last three decades, providing a variety of methods and guidelines (Aleotti and Chowdhury 1999 ; Fell et al. 2008 ; Reichenbach et al. 2018 ). In Europe, the European Landslide Expert Group deals with landslide susceptibility assessment at the European and national scale, providing specific guidelines and developing models based on available data on predisposing factors data and landslide inventories (Hervas 2007; Gunther et al. 2014, Herrera et al. 2018 ; Wilde et al. 2018 ). Günther et al. ( 2013 ), indicate that the European Commission's strategy for the prevention of natural and man-made disasters acknowledges the significance of landslide zoning through spatial susceptibility assessments (EC 2009 ) which subsequently contribute to the establishment of coherent and consistent risk assessment methodologies (EC 2010 ), thus allowing the European member countries to create comparable maps. Several countries in Europe have drawn up landslide susceptibility maps at a national level including France (Malet et al. 2013 ), Italy (Trigila et al 2013 ), Romania (Balteanu et al. 2020) and Austria (Lima et al. 2017 ). There is a growing interest in the study of landslide susceptibility at larger scales, such as regional or local, resulting in numerous of studies worldwide. Greece is one of the European countries most severely affected by landslides on an annual basis. From the earliest documented landslides in Greece dating back to the early 1900s, Tsivlos landslide in Peloponnese. It was one of the largest and most destructive landslides on record in the country which also led to the damming of the Krathis River course and the formation of two lakes (Zygouri and Koukouvelas 2019 ). According to the historical archive of the Hellenic Survey of Geology and Mineral Exploration (H.S.G.M.E., former I.G.M.E.) some of the first official landslide technical reports were established during the 1920s and since then landslide phenomena all over the country have been studied systematically by the company, to evaluate the nature of the hazard and the damages to human life, buildings, and lifeline facilities. However, the interest of the scientific community in the study of landslides began in the 1990s and was initially directed towards the developments of databases (Koukis and Ziourkas 1991 ; Hadzinakos et al. 1991 ) followed by statistical analysis aiming to estimate landside hazard in Greece (Koukis et al. 1994 ; 1996 ; 1997 ; 2005 ). Early examples of research on national scale susceptibility assessment in Greece come from Sabatakakis et al 2013 and Sakkas et al. ( 2016 ). The first study applied multivariate statistical analysis on 1635 landslides in conjunction with 10 causative factors. The second used a knowledge-driven method in order to derive the proper weights for 5 distinct factors due to the lack of a national inventory. Moreover, in last decade an amount of literature related on landslide susceptibility analysis in regional scale has been published for numerous landslide prone areas in Greece applying various methods (e.g. Bathrellos et al. 2009 ; Ferentinou and Chalkias 2013 ; Kouli et al. 2014 ; Ilia and Tsangaratos 2016 ; Polykretis et al. 2018 ; Ntelis et al. 2019 ; Kavoura and Sabatakakis 2020 , Kontoes et al. 2021 , Tavoularis et al. 2021 ; Nefros et al. 2023 ). A variety of techniques have been established for the implementation of landslide susceptibility, depending on the purpose to which the landslide zoning is to be applied, the scale of assessment or the quality of data (Fell et al. 2008 ; Corominas et al. 2014 ). These techniques may be either qualitative or quantitative (Soeters and Van Westen 1996 ). Quantitative methods are generally regarded as objective, as they produce numerical estimates, whereas qualitative methods are considered to be subjective, based on the expert’s judgment (Reichenbach et al. 2018 ). The advantages and limitations of these approaches have been summarized by Aleotti and Chowdhury ( 1999 ), Pardeshi et al. ( 2013 ), and Reichenbach et al. ( 2018 ). Soeters and van Westen ( 1996 ) suggest that a national scale map is created to give a general overview of problem areas across an entire country, serving to inform national policy makers and the general public. On the other hand, regional scale maps are designed for use by planners in the early phases of regional development projects or engineers evaluating possible constraints in landslide prone areas in the development of large engineering projects and regional development plans. Supporting this view, Corominas et al ( 2014 ) set the range of > 1:250,000 for national scale maps and the range of 1:250,000 to 1:25,000 for regional scale analysis. Landslide susceptibility mapping relies on a rather complex knowledge of slope movements and their controlling factors. The basic point of view is that landslide susceptibility determines the spatial distribution of landslide predisposing factors such as geological formations, hydrological conditions and topography and identifies zones prone to landslides (Radbruch 1970 ). Selecting the landslide predisposing factors should be taken into account: (i) the scale of the analysis, (ii) the characteristics of the study area, (iii) the landslide type, and (iv) the availability of data at the appropriate scale (van Westen et al. 2008 ). The reliability of landslide susceptibility maps mainly depends on the quantity and quality of available data, the working scale, and the selection of the appropriate methodology for analysis and modelling (Baeza and Corominas 2001 ). The HSGME represents the primary source of landslide information in Greece, providing significant assistance to Civil Protection Authorities. In this context, HSGME investigates and monitor landslides and other ground failures at local, regional and national scale, using a combination of field survey and remote sensing techniques. The aforementioned parameters facilitated the development of the National Landslide Database. However, for many years the company had not published landslide susceptibility maps at any scale. For this reason, the GEOKA project (2018–2023) involved studies in the field of natural hazards, focusing among others on landslide phenomena. In the framework of the project, the National Database of Landslides was updated with new records and aligned with European standards as well as data accuracy and integration were also enhanced through field surveys in reactivated landslides. The outcome of the above was a revised inventory map of the landslides that had occurred at the national scale, which will serve as the foundation for further analysis in landslide susceptibility assessment. Hence that the GEOKA project has been designed to fill the gap in research on susceptibility assessment in Greece, with due consideration of the updated National Database of landslides. This paper presents methodology adopted by the project in order to provide landslide susceptibility assessments in regional and national scale. The Analytic Hierarchy Process (AHP) and Frequency Ratio (FR) methods were employed in each study area for six common-used predisposing factors (lithology, slope, land use, distance from faults, distance from hydrographic network and road density). The Landslide Susceptibility Index (LSI) was used for mapping landslide susceptibility level, with validation performed via ROC curves. 2. Study areas 2.1. Greek territory Greek territory covers an area of approximately 131,000 km 2 where mountainous regions with altitudes ranging between 600 and 2900 m represent the 33% of the county’s area. It is well known that in Greece several geotectonic zones are distinguished by fundamental differences both in lithology and structure. These geotectonic zones broadly align with the trend of the main mountain ranges (NNW-SSE axis) thus shaping the landscape of the country (Fig. 1). The western and central parts of the country mainly include sedimentary formations not older than Mesozoic associated with E-W compression. The tectonic movements of this period refer to discontinuous westward and eastward orogenic migration, characterized by intense collisional tectonics producing tectonic nappes and slices, which are thrust on top of each other from east to west (Mountrakis et al 1983 ; Doutsos and Kokkalas 2001 ; Mantovani et al 2022 ; Sboras et al. 2022 ). The eastern part of the country is occupied by metamorphic rocks extended in age from Paleozoic to recent as well as igneous and metamorphic rocks of varying age (Koukis et al. 2005 ). By the end of Alpine orogenesis, post-alpine Neogene sediments were deposited into trenches that have been earlier created by tectonism. Previous studies have explored the relationship between landslides and geological formations in Greece. In 1982, Andronopoulos, investigated some large-scale landslides located in western Greece, thus identified certain factors contributing to instability conditions in upper cretaceous limestones and flysch of Olonos-Pindos zone. According to Koukis et al. ( 2005 ) this geotectonic zone exhibits the highest frequency of landslide occurrences in Greece, exceeding 40% followed by Pelagonean zone, 39%. Koukis et al. ( 2005 ) identified that in Western-Central Greece, the highest percentages (60–70%) of landslides are recorded in the flysch outcroppings while in Eastern Greece the landslides recorded equal to 80–85%, involve Neogene sediments. In addition, Neogene marls and flysch formations are also related on numerous landslides in areas such as Evia Island (Ilia and Tsangaratos 2016 ) and the northern part of Achaia prefecture (Kavoura and Sabatakakis 2020 ). It is worth to note that many researchers have observed that the majority of serious instability phenomena are often detected in the upper weathered zone of flysch and Neogene sediments which have a thickness ranging up to some meters (Koukis et al. 2015; Ilia and Tsangaratos 2016 ; Kavoura and Sabatakakis 2020 ). 2.2. Pilot areas Regional scale susceptibility assessment was carried out in 4 well-documented landslide prone areas in Greece. In order to evaluate the efficacy of both quantitative and qualitative analysis, the assessments were conducted in pilot areas that differed significantly in terms of size and geoenvironmental settings. Moreover, according to the archive of HSGME, many landslides have been recorded in these areas over many years, frequently affecting residential areas and transportation routes (Table 1 ). The majority of the records refer to rainfall-induced landslides. Table 1 Differences and specifications of selected areas Pilot areas Total area (km 2 ) Landslides Geological formations Corfu Island 590 203 Alpine formations of Ionian Zone – Neogene sediments Pelion province (Magnisia Prefecture) 905 539 Metamorphic basement of Pelagonian Zone Evritania Prefecture 1870 483 Alpine formations of Olonos-Pindos zone Achaia Prefecture 3275 368 Neogene and Quaternary sediments Corfu island Corfu island, with a total size of approximately 590 km 2 , belongs to the complex of Ionian Islands, in the Ionian Sea in western Greece (Fig. 1). In this area a NNW–SSE trending system represents the convergence between the Apulian Platform and the Hellenic foreland, with Corfu lying on the northwestern edge of the Hellenic Fold and Thrust Belt. Additionally, the north part of the island is characterized by a major E–W-striking right-lateral structure that crosses Corfu from coast to coast, Southern Salerno–North Corfu fault zone, which has resulted in the displacement of N–S-trending fold axes and thrusts (Sakkas et al 2022 ). Due to tectonics, the morphological relief is more pronounced in the northern part. Concerning the geology, formations from the Ionian geotectonic zone, mainly limestones, as well as Neogene and Quaternary formations, contribute to the geological structure of Corfu. In general, the geological structure is characterized by intense folded structures, reverse faults, large transverse ruptures and uplifting movements. Up to now, little research has been done in the field of landslides in this particular region of the country (Konstantopoulou et al, 2019 , Kavoura et al, 2024 ). However, updated information reveals a long history of landslides, affecting both residential areas and infrastructures. According to Konstantopoulou et al. ( 2019 ), landslide phenomena in Corfu mainly occur in Neogene sediments, accounting for over 65% of the occurrences while 76% of the recorded landslides are located within residential areas and the road network and the rest of 24% within forests and cultivated lands. Evritania region The Evritania region is situated in central Greece, encompassing the southern region of the Pindos Mountain range, with an area spanning 1870 km 2 (Fig. 1). The area's tectonic activity is notable, primarily attributed to the presence of the Pindos Mountain range, characterized by extensive folds and successive thrusts. This tectonic activity, coupled with neotectonic processes and lithological factors, has contributed to the development of an intense relief and a complex hydrological network. The geological structure of the region is composed of formations from the Olonos-Pindos geotectonic zone, (flysch, limestone, chert) along with Quaternary formations. Landslide occurrences are predominantly associated with formations of the flysch and they are mostly in the completely weathered zone. Numerous studies have been conducted in the Evritania region regarding landslides including landslide susceptibility assessments (Rozos and Apostolidis 2004 ; Marinos et al 2015 ; Atzemoglou et al 2016 ; Ntelis et al 2019 ; Krassakis et al 2020 ; Kontoes et al 2021 ). Investigating landslide susceptibility in a selected part of Evritania region, Atzemoglou et al 2016 reported that several settlements are located within areas characterized by “Very High susceptibility” and the majority of the road network lies in landslide-prone areas. Ntelis et al. ( 2019 ), based on a vague combination of three models (Analytical Hierarchy Process, Frequency Ratio and Fuzzy Logic) concluded that approximately the 45% of the Evritania region is classified in “High” and “Very High” susceptibility zones. Achaia region The largest of the pilot areas, 3275 km 2 , Achaia prefecture belongs to the north-western part of Peloponnese peninsula. This area has been characterized as one of the most susceptible to landslides parts in the country (Chalkias et al. 2014 ) where experienced some of the largest as devastating landslides have ever recorded in Greece: Panagopoula landslide (Koukis et al. 2009 ; Sabatakakis et al. 2015 ; Kavoura et al. 2016 ), Platanos landslide (Tsiambaos et al. 2015 ; Kavoura et al. 2020), Karya landslide (Sabatakakis et al. 2005 ; Koukis et al. 2007 ; Tsiabaos et al. 2015) Tsivlos landslide (Zygouri and Koukouvelas 2019 ; Tichavský et al. 2023 ). With regard to the geological settings, the landscape evolution in the north, is controlled by the neotectonic action of the graben, which forms the Corinthian Gulf. However, Achaia is also distinguished by a complex geological structure due to the fact that Gavrovo zone formations being covered by overthrusting from the Pindos zone. Therefore, formations from three geotectonic zones (Olonos–Pindos, Gavrovo–Tripolis and Ionian) participate in the geological structure of Achaia prefecture. More precisely, the bedrock geology comprises mainly carbonates, including limestones, cherts and schists, while a transition zone overlays them, including limestones, shales, cherts and marls, leading to the typical flysch sequence sediments of the Upper Eocene. During the Pliocene and Pleistocene, lacustrine and marine-lacustrine deposits such as marls, sandstones, conglomerates accumulated in the trenches created by the faulting. Subsequently, Quaternary deposits of scree and alluvial fans deposited on the Neogene sediments and flysch formation (Rozos 1989 ; Degnan and Robertson 1998 ; Doutsos et al. 2000 ; Koukis et al. 2009 ). Pelion peninsula The study area extends over 905 km 2 in Magnisia Prefecture, central Greece in a NW-SE direction between the Aegean Sea to the east and the Pagasetic Gulf to the south. The area of NE Pelion is characterized by steep morphology which contributes to the occurrence of numerous landslide phenomena in the area. The intense morphological relief observed can be attributed to both past geodynamic processes and more recent tectonic movements. It is composed mainly of alpine formations (Galanakis 1997 ). Upper Paleozoic-Early Triassic (meta)clastic deposits together with thick Pelagonian carbonates of Triassic and Jurassic age compile the bedrock of the Pelion peninsula (Porkoláb et al. 2019 ). An abundance of technical reports points out that landslides in this area triggered by rainfall caused extensive damages on the road network and buildings. The vast majority, 95%, of landslides occur in soil formations with a relatively minor occurrence, approximately 5%, associated with rock formations (Lekkas et l 1991). 3. Material and methods 3.1. Landslide inventory The reference National Geodatabase of landslides, based on historical archives was the main source of information. The necessity of digitizing the data of the numerous landslide phenomena that have been studied over time by the Hellenic Geological Survey for more than 70 years, was demonstrated as soon as technology made the relevant tools available. The compilation of the database started in 2006 through indexing of the oldest analogue archive reports and their entry into a database. In 2012, Eeckhaut and Hervás pointed out that 2,200 occurrences had been recorded in the National database of landslides. This number would increase in the following years reaching almost 3,000 landslides (Herrera et al. 2018 ). Nowadays, the database of Greece summarizes over 5,570 landslides (December 2023). For each landslide record, a range of information is available, including details of the geological conditions, information related to location, the date of occurrence or investigation, the landslide type, the triggering factors, as well as the impact of each landslide, are available. However, this data contains a level of uncertainty about the geospatial accuracy, that depends on the age of the report. Thus, the accuracy of the landslide locations depends on how they are referred to in the historical documents (technical reports). For this reason, information was evaluated by field reconnaissance surveys at the selected regions during the period 2019–2023, to increase the accuracy and the reliability of the inventory as well as the landslide susceptibility maps (Fig. 2). In particular, the reconnaissance surveys were conducted with the following objectives i) to examine the landslide activity and potential re-activations, ii) to monitor the evolution of landslides in conjunction with infrastructure, remedial works, residential areas and the physical environment, iii) to verify and correct the geometry previously digitized landslides, iv) to record new landslides in areas without prior information and v) to maintain the landslide database in a current state. Furthermore, additional geospatial assessments were performed randomly throughout the geodatabase to ascertain the accuracy of the final inventory and to address specific instances of interpretation. According to the above, a multi-temporal landslide inventory of about 5,570 landslides was compiled for conducting a national scale landslide susceptibility assessment. For the purpose of analysis, the available landslide records were transformed into a point layer. Their spatial distribution presents the initial step towards susceptibility assessment (Guzzetti et al. 2012 ). Moreover, regarding the regional scale analysis, four sub-inventories were derived from the national inventory. The total number of landslides in selected areas represent approximately the 27% of the national landslide inventory (Table 1 ). However, a second inventory independent of the main one was mandatory to be generated for each area, under the purpose of verification. Here, the secondary landslide inventories were drawn up as proposed by Remondo et al. ( 2003 ). Thus, for all the cases in Greece (Fig. 3a), and Corfu island, the analysis was made using landslides activated in a certain period (before 2020), and the validation was performed by means of landslides that occurred in a different period (after 2020) (Fig. 3b). In all other study areas, the initial inventory was randomly divided into two groups, one for the susceptibility analysis (70% of the total) and one for validation (30% of the total). In particular, these inventories in Achaia, Pelion and Evritania are shown in Figs. 3c, 3d and 3e respectively. 3.2. Predisposing factors For the assessment of susceptibility at both national (1:500,000) and regional (1:50,000) scales, six key-role predisposing factors were selected that are often used in such analyses based on literature review and experience in landslide studies (Sabatakakis et al. 2013 ; Sakkas et al. 2016 ; Malet et al. 2013 ; Grozavu & Patriche 2021 ; Balteanu et al. 2010; Lima et al. 2017 ). The preparation of the appropriate thematic layers is an important step in integrating the spatial coverage of the susceptibility of the area under consideration. For the national landslide susceptibility assessment, 100m spatial resolution thematic maps were used, while for the regional scale analyses the resolution was 50m. For this study, six commonly used predisposing factors were chosen to reflect the general geological, topographic and environmental settings in the study areas. Specifically, the following parameters were considered for regional and nationwide susceptibility assessments. In Fig. 4, examples of thematic layers in both scales are presented. Lithology The Geotechnical Map of Greece, at a scale of 1:500,000 (IGME 1993), which classifies the geological formations into 28 geotechnical units, was used in the national scale analyses. At the the regional scale analyses, an adapted map was created at a scale of 1:50,000 scale map was constructed based on the 1:500,000 scale Geotechnical Map of the IGME, the 1:50,000 scale Geological Sheets of the IGME, data from fieldwork and the parallel use of satellite imagery. Slope angle The slope was derived from the slope map as created by the Digital Surface Model (DSM) of Hellenic Cadastral S.A. with a resolution of 5X5 m. The final slope map was adjusted to a resolution of 50m at both resolution scales, in order to ensure the rendering of the majority of the slope classes. The classification is performed in 6 classes. Land use The classification of land use was based on the Corine Land Cover 2018 plan, which is part of the Pan-European Land Use Database. This project includes the mapping of land use/land cover of various European countries (including Greece) at a scale of 1:100,000. For Greece 42 land use categories have been identified. Road network This factor used as an indicator of the impact of the road network on landslide occurrence. The choice of density instead of distance from the road axis is expected to give more objective results on the effect of the factor, while isolating densely populated areas where both the occurrence of events is not expected and extensive man-made interventions may have been implemented. The road network used was derived from the OpenStreetMap (OSM) open data ( https://www.openstreetmap.org/ ). OSM as an information source is suitable for use by public authorities due to its free use of unrestricted data, its completeness and its interoperability stems from users. Faults The presence of tectonic structures (faults, thrusts, folds) affects the mechanical characteristics of the rock mass and geological formations in the area where they are located. For this reason, and given the scale of the susceptibility analysis, the traces of tectonic faults (faults, thrusts etc) as recorded in the Geological Sheets of IGME were used. This digital thematic layer in vector format was provided by the Department of General Geology, Geological Mapping and Applications of HSGME. A 100m resolution and 1000m radius of influence tectonic structure density map was created for the national scale analyses. For the regional scale analyses, the concept of distance from faults was used, which included buffer zones of a 50m constant step. Hydrographic network Rivers and the erosion of their slopes is one of the most common factors causing landslides, especially in areas with intense morphological relief and a dense hydrographic network with deep valleys. Two different hydrographic network backgrounds at different scales were provided by the Department of Hydrogeology and Hydrology of HSGME. The hydrographic network at a scale of 1:250,000 was used for the national scale analyses while for the regional scale analyses employed the network mapping at the water district level was used. Similar to the factor of tectonic structures, a density map and influence zone map were used respectively. 3.3. Methods The methodology followed in the current work is the comparison and the evaluation of the landslide susceptibility that derived from a statistical method (quantitative analysis) and a qualitative expert-based method. The adopted methodology was applied to model landslide susceptibility separately for each area and both methods to account for the differences in the relative contribution of the landslide causative factors. The landslide susceptibility assessment was carried out with Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) method. The overall susceptibility of an area was determined by synthesizing all the factors using an algebraic approach, resulting in a Landslide Susceptibility Index (LSI) used to map the level of landslide susceptibility. Finally, the validation and the evaluation of results achieved in the previous stage were conducted by ROC curves. The AHP was used to derive factor weights and factor class weights from a pairwise comparison matrix, based on normalized landslide frequency ratios and expert knowledge adjustments. The application of the Analytical Hierarchy Process (AHP) method, developed by Saaty ( 1977 ), has been used by many authors worldwide, as a multi-criteria decision-making method. It involves making binary comparisons of factors within a complex problem. After constructing a hierarchical representation of the problem, the next steps involve pairwise comparisons of factors and subfactors using a nine-point scale in a matrix table. The scale values range from 1 (equal importance) to 9 (extremely stronger importance), with intermediate values such as 2, 4, 6, and 8 indicating intermediate levels of importance (Saaty, 1977 ). Each factor is assessed in relation to every other factor using values from 1/9 to 9. Subsequently, the relative weights for each factor and subfactor in the decision hierarchy are estimated. The consistency ratio (CR) is then calculated to validate the AHP results and prevent arbitrary choices in the matrix. The CR is considered valid if it is equal to or less than 0.1 (10%) (Saaty, 1978 ). The equation (Eq. 1) for calculating the consistency ratio is: CR = (CI/RI)*100 (Eq. 1) where RI is the random consistency index and CI is the average consistency index calculated as (Eq. 2): CI = (λ max – n)/(n – 1) (Eq. 2) where λ max is the maximum eigenvalue of the comparison matrix, and n is the number of factors. The Frequency Ratio (FR) model, as a statistical approach, based on the analysis between distribution of landslides and each landslide-related factor, to reveal the correlation between landslide locations and the factors in a specific area (Lee and Pradhan 2007 ). Therefore, the frequency ratios of each factor class were calculated from their relationship with landslide events. According to the method, the number of landslides in each class is evaluated and the frequency ratio for each factor class is found by dividing the landslide ratio by the area ratio (Lee and Talib 2005 ). If the ratio (FR) is greater than 1, then the relationship between a landslide and the factor’s class is strong while if ratio is less than 1, the relationship is weak. The Frequency Ratio model (FR) is widely used in the international literature (e.g. Lee and Tallib, 2005; Pradhan and Youssef, 2010 ; Regmi et al. 2014 ; Kavoura et al. 2020) due to its simple application in cases where a comprehensive multi-factor landslide database is available. The validation process is a crucial stage in developing landslide susceptibility models. One of the most widely used quantitative validation methods is the Receiver Operating Characteristic curves (ROC curves). These curves can be used to test the success of the model or the prediction ability. Success rate curves are plotted taking into account the landslides themselves that were used to develop the model (training set). On the other hand, prediction rate curves are built considering independent landslides (validation set) and measure the prediction skill of the model. Many researchers use this method for the evaluation of susceptibility models (Van Den Eeckhaut et al. 2009 ; Akgun 2012 ; Schicker and Moon 2012 ; Ciurleo et al. 2016 ). 4. Results 4.1. Landside susceptibility assessment in Greece Utilizing AHP in the study areas, the calculation commenced with pairwise comparisons of all possible pairs of factors in a matrix based on expert knowledge. Subsequently, values and weights were determined, and in the final step, the consistency index (CI) and consistency ratio (CR) were calculated. The procedure was repeated until the CR ≤ 0.1 (10%). In contrast to the AHP method, the FR method requires a training data set to compute the weights for each factor and its classes. Training sets as well as test sets were generated as described above. The FR model was applied to define weights for each factor, using the ratio of the percentage of landslides in a class of the selected factor to the percentage of the area of this class in the study area. Based on the results of the hierarchy process analysis and FR model (Table 2 ) regional scale susceptibility maps were produced according to LSI calculations. Likewise, the procedure also was run for national scale estimations as presented in Table 2 and Table 3 . As regards the landslide susceptibility maps were produced in regional scale the classification of the susceptibility level is given in a percentage scale of 10%, so that each class differs from the next in terms of the degree of susceptibility, i.e. the probability of a landslide occurring in the area according to the conditions of the area, by 10% (Fig. 5). The lower the percentage the lower the susceptibility level. This classification would be favour to simplify the legibility and the comparison between the landslide susceptibility maps. However, in national scale maps the degree of susceptibility is classified into 5 classes according to the natural breaks method (Fig. 6). The distribution of the data is done in such a way that the average value of each value interval is closest to the values of that interval. This ensures that the value intervals are best represented by their averages and that the data values between these intervals are reasonably close. In this case the susceptibility is described as Very Low, Low, Medium, High and Very High. Table 3 Modified factors of faults and streams for national scale assessments Factor Sub-factors Weight AHP FR CR Faults density 0–0,5 km/km 2 0.082 0.77 0.068 0,5 − 1 km/km 2 0.117 1.62 1–2 km/km 2 0.126 1.18 2–3 km/km 2 0.221 0.82 3–4 km/km 2 0.234 0.82 4–5 km/km 2 0.221 1.2 Density of hydrographic network 0–0,4 km/km 2 0.398 1.04 0.060 0,4 − 0,8 km/km 2 0.239 1.16 0,8 − 1,2 km/km 2 0.160 1.07 1,2 − 1,6 km/km 2 0.094 0.84 1,6 − 2 km/km 2 0.061 0.82 > 2 km/km 2 0.048 0.48 Table 2 AHP weights of sub-factors for regional scale assessments Factors Sub-factors Corfu Evritania Achaia Pelion Greece Weight AHP FR CR Weight AHP FR CR Weight AHP FR CR Weight AHP FR CR Weight AHP FR CR Slope 0 ο -5 ο 0.057 0 0.103 0.055 0 0.097 0.058 0.07 0.075 0.030 0.02 0.081 0.057 0.07 0.032 6 ο -15 ο 0.124 0.7 0.133 1.3 0.119 0.61 0.052 0.42 0.094 0.61 16 ο -30 ο 0.222 3 0.203 1.43 0.289 1.42 0.086 1.54 0.177 1.42 31 ο -45 ο 0.180 1.2 0.172 0.63 0.245 1.25 0.137 1.05 0.322 1.25 46 ο -60 ο 0.293 3.6 0.327 0.62 0.175 1.89 0.236 2.3 0.217 1.89 > 60 o 0.124 0 0.110 0 0.114 1.45 0.459 0 0.133 1.45 Lithology Quaternary loose fine-grained deposits with organics - - 0.099 - - 0.081 - - 0.094 - - 0.064 0.007 0 0.098 Quaternary loose deposits, coarse-grained sediments 0.072 3.61 0.042 1.23 0.015 0.7 Quaternary loose deposits, fine-grained sediments - - 0.030 0 - 0.015 0.3 Quaternary loose deposits of mixed phases 0.080 0.1 0.100 0.48 0.057 0.18 0.063 0 0.066 0,4 Quaternary coherent coarse-grained deposits 0.105 2.5 - 0.063 0 0.055 0 0.045 2.5 Quaternary coherent deposits of mixed phases 0.206 3.4 - 0.031 0 - 0.020 1 Neogene coarse-grained sediments - - 0.073 1.99 - 0.014 1.1 Neogene fine-grained sediments 0.172 2.1 - 0.151 4.9 - 0.059 2.2 Neogene sediments of mixed phases 0.266 1.8 - 0.129 1.26 0.088 0.11 0.1 1.3 Thrace molasse mainly with fine-grained sediments 0.014 0.3 Thrace molasse sediments of mixed phases 0.007 0.1 Molasse deposits of Mesohellenic Trough 0.012 0.3 Flysch (siltstones and sandstones) 0.072 0.5 0.359 1.54 0.142 0.75 0.054 0 0.143 2.6 Flysch (conglomerate and sandstones) 0.117 1.53 0.040 0 0.019 2 Limestones 0.047 0.2 0.033 0 0.041 0 0.045 0 0.029 0.3 Limestones with silex nodule and phacoids 0.190 0.55 0.031 0.32 0.081 1.2 Limestones with chert, schist-chert or schist-marl 0.070 0.66 0.029 6.76 0.020 2 Limestones-Dolimitic limestones-Dolomites 0.036 0 0.050 0 0.042 0.4 Clayey shale and chert 0.059 2.94 0.026 0.029 1 Cherts with marly lists 0.023 1.5 Gypsum or/and breccia (calcareous-dolomitic) 0.014 0.1 - 0.030 0 - 0.015 1.5 Semi-metamorphic rocks - - 0.035 0.189 0.23 0.057 1.5 Metamorphic carbonate rocks - - - 0.151 1 0.038 0.8 Metamorphic rocks - - - 0.281 2.05 0.083 1.3 Acid plutonic rocks 0.011 0.2 Mafic-Ultramafic Igneous rocks - - - 0.041 0 0.013 0.4 Volcanic rocks (lava) 0.011 0.3 Volcanic rocks (lava) - - - 0.033 0 0.011 0.2 Land use Continuous urban fabric 0.017 0 0.099 0.103 0.013 0 0.089 0.015 2.93 0.055 0.011 0.4 Discontinuous urban fabric 0.073 2.2 0.039 2.93 0.026 3.48 0.095 0 0.022 1.9 Industrial or commercial units 0.017 1 0.023 0 0.016 0 0.019 0 0.014 0.3 Road and rail networks and associated land 0.025 0 0.018 0 0.020 0.8 Port areas 0.012 0 0.014 0 0.016 13.24 0.008 0 Airports 0.016 0 0.012 0 0.009 0 Mineral extraction sites 0.039 0 0.037 0 0.025 0 0.023 0.4 Dump sites 0.043 0 0.039 0 0.023 0 0.024 0 Construction sites 0.023 0 0.018 0 0.017 1,3 Green urban areas 0.027 0 0.017 3,2 Sport and leisure facilities 0.074 2.5 0.054 0 0.036 0 0.026 0 0.036 3,3 Non-irrigated arable land 0.046 0 0.043 0 0.031 0,75 0.024 8,94 0.025 0,3 Permanently irrigated land 0.034 0 0.024 0 0.022 0,1 Rice fields 0.015 0 Vineyards 0.028 0 0.059 9,77 0.036 1.4 Fruit trees and berry plantations 0.040 0,69 0.094 0 0.035 1.2 0.1 Olive groves 0.081 0.4 0.060 0 0.054 4,03 0.086 0 0.059 1.6 Pastures 0.035 0 0.097 0 0.033 0 0.038 0 0.023 0.7 Annual crops associated with permanent crops 0.03 0 Complex cultivation patterns 0.097 0.9 0.046 4.03 0.066 3.94 0.067 0 0.056 2 Land principally occupied by agriculture, with significant areas of natural vegetation 0.104 2.5 0.137 6.39 0.086 8.87 0.087 0.47 0.064 4 Agro-forestry areas 0.048 0 Broad-leaved forest 0.046 2.4 0.067 1.32 0.037 5.74 0.064 6.68 0.037 0.8 Coniferous forest 0.071 0.38 0.042 0.6 0.054 0 0.031 0.7 Mixed forest 0.023 0 0.075 0.78 0.031 1.59 0.055 0.02 0.024 0.7 Natural grasslands 0.027 0 0.034 0.15 0.032 0.46 0.030 1.81 0.023 0.4 Moors and heathland 0.040 0.08 0.036 0 0.023 0.3 Sclerophyllous vegetation 0.048 0.3 0.062 0.13 0.048 1.22 0.038 10.22 0.037 0.3 Transitional woodland-shrub 0.025 0 0.039 0.96 0.029 1.8 0.037 0 0.024 1 Beaches, dunes, sands 0.046 0 0.029 0 0.025 0 0.022 0.3 Bare rocks 0.030 0 0.026 0 0.021 0.6 Sparsely vegetated areas 0.029 0.1 0.029 0.38 0.034 0 0.035 0.33 0.025 0.4 Inland marshes 0.013 0 0.011 0 Burnt areas 0.035 0 Salt marshes 0.011 0 0.011 0 0.009 0 Salines 0.011 0 0.009 0 Water courses 0.013 0 0.009 0 0.008 0 Water bodies 0.012 0 0.009 0 0.008 0 Coastal lagoons 0.011 0 0.008 0 Estuaries 0.007 0 Sea 0.01 0 Faults 0-50m 0.515 2.9 0.019 0.091 0.98 0.084 0.094 1.06 0.083 0.381 0.37 0.021 51-100m 0.225 1.9 0.128 0.84 0.166 1.16 0.289 0.4 101-150m 0.130 2.6 0.336 1.07 0.281 1.03 0.183 0.58 151-200m 0.081 1.9 0.243 1.22 0.284 1.46 0.091 0.38 > 200m 0.050 0.8 0.202 0.99 0.175 0.95 0.056 1.38 Road network 0-2km/km 2 0.421 0.3 0.016 0.416 0.38 0.047 0.384 0.29 0.027 0.374 0.23 0.043 0.441 0.29 0.028 2-5km/km 2 0.267 0.5 0.244 3.14 0.258 0.91 0.244 0.72 0.251 1.44 5-10km/km 2 0.143 1.6 0.148 4.1 0.154 3.27 0.157 2.03 0.138 2.54 10-20km/km 2 0.084 3 0.095 2.75 0.102 5.2 0.117 3.95 0.088 5.23 20-40km/km 2 0.052 1.2 0.059 0.98 0.061 0.31 0.068 2.38 0.051 1.61 > 40km/km 2 0.033 0 0.038 0 0.040 0 0.040 0 0.031 0.38 Distance from streams 0-50m 0.444 1.1 0.006 0.388 0.9 0.048 0.381 0.7 0.061 0.419 0.67 0.019 51-100m 0.262 1 0.268 1.08 0.274 1.04 0.250 0.67 101-150m 0.153 1.41 0.167 1.24 0.166 1.28 0.163 0.82 151-200m 0.089 1.38 0.097 0.94 0.113 1.25 0.100 0.55 > 200m 0.053 0.8 0.080 0.91 0.065 0.95 0.067 1.71 4.2. Models evaluation An important stage in landslide susceptibility assessment is to evaluate the effectiveness of the produced landslide susceptibility map. For this purpose, receiver operating characteristic (ROC) curves were used, firstly for checking the reliability of the proposed model (success rate curves) as well as to check the ability of the model to pinpoint landslide-prone areas (prediction rate curves). The accuracy of the model is checked for each area, using the training set and an equal number random set of points free of landslides. The process was repeated for the validation set of landslides in order to find if these independent landslide occurrences were correctly adapted in different susceptible areas. Based on these, the results of AHP and FR methods were compared in order to achieve the accuracy level of each susceptibility model. From the graphs of Fig. 6, show that FR model has better performance than AHP in all cases of correlations (Fig. 7). High performance is actually recognized in both success and prediction checks. The results, as shown in Table 4 , indicate that in Corfu island both models have the ability to correctly predict the susceptible areas, with the FR model being more successful while AUC value is approximately 0.9. Similar results are noted in Pelion where both methods give AUC > 0.7, with the FR model performing better. The susceptibility models in Evritania, the AHP method does not correctly assign the degree of susceptibility, not being able to accurately classify the already recorded landslides. On the other hand, the FR method is considered efficient as AUC > 0.8. Such statistical performance was also observed in Achaia. Finally, in the case on Greek territory a good predictive accuracy was also obtained for the FR method, with AUC > 0.8 in contrast with the results of AHP method. In addition, further analysis on AUC values between success and prediction rates were conducted. Thus, the percentage difference comparison was applied on AUC values of each validation method. The percentage difference was calculated between two number values, AUC success and AUC predict in order to determine how close they are. A high percent difference indicates a large relative change between the values, while a lower percent difference suggests a smaller relative change. Table 4 ROC analysis results for success and prediction rates Study area AUC AHP Difference % AUC FR Difference % Success Prediction Success Prediction Corfu island 0,747 0,706 5,6 0,893 0,906 1,4 Evritania 0,499 0,429 15,1 0,872 0,874 0,2 Achaia 0,614 0,558 9,6 0,864 0,826 4,5 Pelion 0,771 0,745 3,4 0,838 0,879 4,8 Greece 0,673 0,689 2,3 0,871 0,854 2,0 5. Discussion Losses and damages associated with landslides can be reduced significantly if decision makers of all levels of government take well-informed actions before a disaster occurs and respond appropriately after a disaster. Landslide susceptibility assessment keeps a fundamental role in landslide risk management. From this point of view, national, regional and local scale landslide susceptibility maps are going to be a very important tool for decision-makers such as civil protection authorities and stakeholders. This study based on the updated National Landslide Database of H.S.G.M.E. proceeds to estimate the effectiveness of specific areas in landslides, through modelling and compilation of reliable and accurate susceptibility maps. As result, landslide susceptibility assessments at national (1:500,000) and regional scale (1:50,000) were contacted for Greece. Comparing the results of statistical and knowledge-based methods were applied for landslide susceptibility assessment, a new national-scale susceptibility map is presented. In addition, regional scale analyses also were discussed regarding four well-known landslide prone regions in Greece. According to the ROC analysis, FR model seems to have better performance than AHP model. Whereas the area under curve (AUC) value tends to be higher with respect to unity (1), the accuracy of the model increased. Figure 8 compares the results obtained from ROC analysis to verify the ability of the model to correctly classify landslides into susceptibility zones (success rate) with those that verify ability of the model to pinpoint landslide-prone areas (prediction rate). Interestingly, the relationship between success and prediction values has a positive correlation for both models (AHP and FR). However, the FR dataset displays a stronger relationship between success and prediction values than AHP with AUC value stays above 80% in any case. These results further support the idea that FR model provides sufficient landslide susceptibility mapping in regional and national scale. Another important finding is that the calculated weights for every class of each factor can describe the most landslide-prone areas by suggesting crucial combinations of factors occur in specific regions. It is important to mention that these factors are strongly connected with the mapping accuracy of thematic layers. In Greece, the flysch formation seems to be the most susceptible to landslides, which is in agreement with previous studies (Koukis et al 2005 ; Sabatakakis eta al. 2012; Sakkas et al 2016 ). Landslide susceptibility assessment in the island of Corfu highlights the impact of landslide hazard in highly residential and touristic areas. Apart from the statistical checks of the model’s prediction accuracy, in Corfu island, a retrospective evaluation of landslide susceptibility maps was evaluated based on a landslide event triggered by heavy rainfalls in the year 2022 (Corominas et al. 2014 ; Fleuchaus et al. 2021 ). On 11 to 12 December 2022 an extreme rainfall event hit the north-western part of Greece including the island of Corfu. These days numerous landslides were activated at Chlomos and Agios Dimitrios villages, at southern eastern part of island. Comparing landside locations with the susceptibility map from the FR method will see that they were activated in areas with very high susceptibility level over 70%, despite the fact that the scale is 1:50000. Lower level of accuracy indicates the AHP model (Fig. 9). While Hellenic Survey of Geology and Mineral Exploration HSGME serves the public and the authorities by providing reliable scientific information and thus minimizing loss of life and property from natural disasters, this research could be a basic tool for managing a sustainable hazard and risk mitigation program in landslide prone area. Declarations Acknowledgements This study was conducted in the framework of the Operational Program entitled \"Competitiveness, Entrepreneurship and Innovation (2015-2020), Project «Studies and researches support to the energy sector, industry and entrepreneurship», Sub-Project «Susceptibility assessment of landslides in the Greek territory - Volcanic study and risk assessment», financed by the European Regional Development Fund. Ethical approval: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Conflict of interest : The authors declare that they have no conflict of interest. References Akgun Α (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey. 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Evritania Prefecture: A Case Study in Greece. Journal of Geoscience and Environment Protection, 7:206-220. https://doi.org/10.4236/gep.2019.78015 Pardeshi SD, Autade SE, Pardeshi SS (2013) Landslide hazard assessment: recent trends and techniques. Springer Plus 2(1):523. https://doi.org/10.1186/2193-1801-2-523 Polykretis C, Faka A, Chalkias C (2018) Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece. Geosciences 8, 261. https://doi.org/10.3390/geosciences8070261 Porkoláb K, Willingshofer E, Sokoutis D, Creton I, Kostopoulos D, Wijbrans J (2019) Cretaceous-Paleogene tectonics of the Pelagonian zone: Inferences from Skopelos island (Greece). Tectonics, 38: 1946–1973. https://doi.org/10.1029/2018TC005331 Pradhan B, Youssef A (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3:319–326. https://doi.org/10.1007/s12517-009-0089-2 Radbruch DH (1970) Map of relative amounts of landslides in California. US Geological Survey Open-File Report 70-1485, 36 p, map scale 1:500,000. US Geological Survey Open-File Report 85–585 Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7:725–742. https://doi.org/10.1007/s12517-012-0807-z Reichenbach P, Rossi M, Malamud B, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180: 60-91 Remondo J, González A, Díaz de Terán JR, Cendrero A, Fabbri A, Chung CJF (2003) Validation of landslide susceptibility maps; examples and applications from a case study in northern Spain. Natural Hazards, 30 (3): 437-449. DOI: 10.1023/B:NHAZ.0000007201.80743.fc Rozos D (1989) Engineering-geological conditions in the Achaia County. Geomechanical characteristics of the Plio-pleistocene sediments. PhD thesis, University of Patras, Greece, pp 453 (In Greek) Rozos D, Apostolidis E (2004) Engineering geological investigation of slope failures in Paleo Mikrohorio Evritania Pr., aiming at its safe residential development. Bulletin of the Geological Society of Greece, 36(4):1806–1815. https://doi.org/10.12681/bgsg.16651 (In Greek) Saaty T (1977) A Scaling Method for Priorities in Hierarchical Structures, Journal of Mathematical Psychology, 15:234-281. Saaty T (1978) Modeling Unstructured Decision Problems-The Theory of Analytical Hierarchies Mathematics and Computers in Simulation. XX:147- 158. Sabatakakis N, Koukis G, Mourtas D (2005) Composite landslides induced by heavy rainfalls in suburban areas: City of Patras and surrounding area, western Greece. Landslides, 2(3):202-211. DOI: 10.1007/s10346-005-0002-3 Sabatakakis N, Koukis G, Vassiliades E, Lainas S (2013) Landslide susceptibility zonation in Greece. Nat Hazards 65(1):523–543. https://doi.org/10.1007/s11069-012-0381-4 Sabatakakis N, Tsiambaos G, Rondoyanni TH, Papanakli S, Kavoura K (2015) Deep-seated structurally controlled landslides of Corinth Gulf rift zone, Greece: the case of Panagopoula Landslide, 13th ISRM Congress Proceedings - Int’l Symposium on Rock Mechanics - Innovations in Applied and Theoretical Rock Mechanics. ISBN: 978-1-926872-25-4, p651, 10p Sakkas G, Misailidis I, Sakellariou N, Kouskouna V, Kaviris G (2016) Modeling landslidesusceptibility in Greece: a weighted linear combination approach using analytichierarchical process, validated with spatial and statistical analysis. Nat Hazards 84(3):1873–1904. https://doi.org/10.1007/s11069-016-2523-6 Sakkas V, Kapetanidis V, Kaviris G, Spingos I, Mavroulis S, Diakakis, M Alexopoulos JD, Kazantzidou-Firtinidou D, Kassaras I, Dilalos S et al. (2022) Seismological and Ground Deformation Study of the Ionian Islands (W. Greece) during 2014–2018, a Period of Intense Seismic Activity. Appl Sci 12: 2331. https://doi.org/10.3390/app12052331 Sboras S, Pavlides S, Kilias A, Galanakis D, Chatziioannou A, Chatzipetros A (2022) The Geological Structure and Tectonic Complexity of Northern Thessaly That Hosted the March 2021 Seismic Crisis. Geotechnics . 2(4):935-960. https://doi.org/10.3390/geotechnics2040044 Schicker R, Moon V (2012) Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 161–162 :40–57. DOI: 10.1016/j.geomorph.2012.03.036 Soeters R, van Westen C J (1996) Landslides, investigation and mitigation. Transportation Research Board, National Research Council, Special Report, 247: 129-177 Tavoularis N, Papathanassiou G, Ganas A, Argyrakis P (2021) Development of the Landslide Susceptibility Map of Attica Region, Greece, Based on the Method of Rock Engineering System. Land. 10(2):148. https://doi.org/10.3390/land10020148 Tichavský R, Fabiánová A, Koutroulis A, Spálovský V, Vala O (2023) Recent debris-flow activity on the 1913 Tsivlos landslide body (Northern Peloponnese; Greece), CATENA, 231. https://doi.org/10.1016/j.catena.2023.107318 Trigila A, Frattini P, Casagli N, Catani F, Crosta G, Esposito C, Iadanza C, Lagomarsino D, Scarascia G, Segoni S, Spizzichino D, Tofani V, Lari S (2013) Landslide susceptibility mapping at national scale, the Italian case study. In: Margottini C, Canuti P, Sassa K (Eds.), Landslides Science and Practice. Vol. 1. Springer, Berlin,Heidelberg, pp. 287–295 Tsiambaos G, Sabatakakis N, Rondoyanni Th, Depoundis N, Kavoura K (2015) Composite landslides affecting flysch and Neogene weak rock formations induced by heavy rainfalls. 13th ISRM Congress Proceedings - Int’l Symposium on Rock Mechanics - Innovations in Applied and Theoretical Rock Mechanics. ISBN: 978-1-926872-25-4, p651, 10p Van Den Eeckhaut M, Reichenbach P, Guzzetti F, Rossi M, Poesen J (2009) Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Nat Hazards Earth Syst Sci 9:507–521 van Westen CJ, Castellanos E, Kuriakose S (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview, Engineering Geology, 102(3–4): 112-131. https://doi.org/10.1016/j.enggeo.2008.03.010 Wilde M, Günther A, Reichenbach P, Malet J P, Hervás J (2018) Pan-European landslide susceptibility mapping: ELSUS Version 2. Journal of Maps, 14(2):97–104. https://doi.org/10.1080/17445647.2018.1432511 Zygouri V, Koukouvelas IK (2019) Landslides and natural dams in the Krathis River, north Peloponnese, Greece. Bull Eng Geol Environ 78:207–222. https://doi.org/10.1007/s10064-017-1225-yz Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 19 Aug, 2025 Editor assigned by journal 02 Aug, 2024 First submitted to journal 31 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4838383\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":502522111,\"identity\":\"9a048621-31c2-4844-a100-702330745799\",\"order_by\":0,\"name\":\"Katerina Kavoura\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACZjYog72BZC08B4i2BqZFIoFIDfztbImfeSoO5/PPfHzw49ccGwb5aALWSRxmOyzNc+aw5YzbacnSstvSGAzPEbLuMHuD5My2wwYMt3MMpCW3HWYw7CGgQ/4we/NPkBb5m+c//5bc9p+wFoPDbMckPgK1GNzgYZP8uO0AgzwPAS2Gh9nSLD6cSTcwPJNmZs24LZnHgJAWufPHjG8kVFgbyB0//Pjmz212cvKEHIYCmIEW8BgcIEUL4w8gId9AipZRMApGwSgYCQAAwOZCEkFMl5sAAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0000-0002-0736-3803\",\"institution\":\"Hellenic Survey of Geology and Mineral Exploration\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Katerina\",\"middleName\":\"\",\"lastName\":\"Kavoura\",\"suffix\":\"\"},{\"id\":502522112,\"identity\":\"297fce18-54bd-4cf6-a458-d4ddaafa5406\",\"order_by\":1,\"name\":\"Spanou Natalia\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hellenic Survey of Geology and Mineral 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00:36:00\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4838383/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4838383/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":90040770,\"identity\":\"63275c3e-1556-4cc5-9585-ea19facd5a87\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 16:54:48\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":9102785,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMorphological overview of the Greek territory and pilot areas\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4838383/v1/5cd2365a20579edffd8132dc.jpg\"},{\"id\":90040126,\"identity\":\"82edf7c8-7200-4b04-b421-44606c793a7b\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 16:46:48\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":266008,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLandslide examples from pilot areas a) Complex landslide in weathering mantle, Evritania region, b) Earth fall, Achaia region, c) Earth slide, Corfu island and d) Rock slide, Pelion province\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4838383/v1/e3767472287065b183662220.jpg\"},{\"id\":90041350,\"identity\":\"224906d8-d444-4060-a316-e277588c2fcb\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 17:02:48\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1170129,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eInventory maps for study areas a) Greek territory, b) Corfu island, c) Achaia prefecture, d) Pelion province and e) Evritania prefecture\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4838383/v1/c2cca3b3d4e1c6daad6ff3a5.jpg\"},{\"id\":90040127,\"identity\":\"817d28f8-2239-4ca7-a004-87ebd579ae40\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 16:46:48\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":973283,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePredisposing factors for national scale assessment in Greece a) lithology, b) slope, c) land use, d) road network density, e) faults density, f) Hydrographic network density, and regional scale analysis in Corfu island, g) lithology, h) slope, i) land use, j) road network density, k) distance from faults and l) distance from hydrographic network.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4838383/v1/6039033ba5c35a29baa78b9a.jpg\"},{\"id\":90041352,\"identity\":\"1db35855-2a80-4f94-8530-a2618515d51e\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 17:02:48\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1198006,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRegional scale landslide susceptibility maps (1:50,000) a) Corfu island, b) Evritania prefecture, c) Achai prefecture and d) Pelion province.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4838383/v1/31ead27062e24f15432df437.jpg\"},{\"id\":90040129,\"identity\":\"292c05ec-0afa-4e76-a997-3beb07d7920a\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 16:46:48\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":696437,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLandslide susceptibility map of Greece (1:500,000) based on a) FR method and b) AHP method\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig6.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4838383/v1/8edda872cb6ad67070a9cffa.jpg\"},{\"id\":90040133,\"identity\":\"87b4c066-0e08-4ebf-8e8a-5d147e04d91f\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 16:46:48\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":473557,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eROC curves graphs for Corfu island a) Success rate, b) Prediction rate; Evritania c) Success rate, d) Prediction rate; Achaia e) Success rate, f) Prediction rate; Pelion g) Success rate, b) Prediction rate and Greece i) Success rate, j) Prediction rate\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig7.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4838383/v1/61b459c6a5a746dab1e065ac.jpg\"},{\"id\":90040766,\"identity\":\"c64476c1-8b5a-45f2-a70b-4ee05fb3409d\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 16:54:48\",\"extension\":\"jpg\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":57204,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAccuracy correlations for the models\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig8.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4838383/v1/ac627bb38bee73173af1c1c8.jpg\"},{\"id\":90040768,\"identity\":\"d137290c-8686-4ba8-a4bd-58854a379e48\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 16:54:48\",\"extension\":\"jpg\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":483286,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRetrospective evaluation of susceptibility maps in Corfu island a) Locations of recent rainfall-induced landslides, b) FR model, c) AHP model\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig9.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4838383/v1/658f0bbdfc08b032563c7d7a.jpg\"},{\"id\":90042323,\"identity\":\"7f226f9c-f0cc-459b-a787-0cde39c3ca44\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 17:18:53\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":15736436,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4838383/v1/2deec8c1-2edd-42a7-b7cf-2fd43bc5338f.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Landslide Susceptibility Assessment in Greece: work in regional and national scale\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eLandslide susceptibility assessment is a commonly used and essential procedure for indicating and classifying the spatial distribution of landslide prone areas. An amount of literature has been published on this topic over the last three decades, providing a variety of methods and guidelines (Aleotti and Chowdhury \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Fell et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Reichenbach et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In Europe, the European Landslide Expert Group deals with landslide susceptibility assessment at the European and national scale, providing specific guidelines and developing models based on available data on predisposing factors data and landslide inventories (Hervas 2007; Gunther et al. 2014, Herrera et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Wilde et al. \\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). G\\u0026uuml;nther et al. (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), indicate that the European Commission's strategy for the prevention of natural and man-made disasters acknowledges the significance of landslide zoning through spatial susceptibility assessments (EC \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e) which subsequently contribute to the establishment of coherent and consistent risk assessment methodologies (EC \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e), thus allowing the European member countries to create comparable maps. Several countries in Europe have drawn up landslide susceptibility maps at a national level including France (Malet et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), Italy (Trigila et al \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), Romania (Balteanu et al. 2020) and Austria (Lima et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). There is a growing interest in the study of landslide susceptibility at larger scales, such as regional or local, resulting in numerous of studies worldwide.\\u003c/p\\u003e\\u003cp\\u003eGreece is one of the European countries most severely affected by landslides on an annual basis. From the earliest documented landslides in Greece dating back to the early 1900s, Tsivlos landslide in Peloponnese. It was one of the largest and most destructive landslides on record in the country which also led to the damming of the Krathis River course and the formation of two lakes (Zygouri and Koukouvelas \\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). According to the historical archive of the Hellenic Survey of Geology and Mineral Exploration (H.S.G.M.E., former I.G.M.E.) some of the first official landslide technical reports were established during the 1920s and since then landslide phenomena all over the country have been studied systematically by the company, to evaluate the nature of the hazard and the damages to human life, buildings, and lifeline facilities. However, the interest of the scientific community in the study of landslides began in the 1990s and was initially directed towards the developments of databases (Koukis and Ziourkas \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e; Hadzinakos et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e) followed by statistical analysis aiming to estimate landside hazard in Greece (Koukis et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e; \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e; \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e; \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). Early examples of research on national scale susceptibility assessment in Greece come from Sabatakakis et al \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e and Sakkas et al. (\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The first study applied multivariate statistical analysis on 1635 landslides in conjunction with 10 causative factors. The second used a knowledge-driven method in order to derive the proper weights for 5 distinct factors due to the lack of a national inventory. Moreover, in last decade an amount of literature related on landslide susceptibility analysis in regional scale has been published for numerous landslide prone areas in Greece applying various methods (e.g. Bathrellos et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Ferentinou and Chalkias \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Kouli et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Ilia and Tsangaratos \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Polykretis et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Ntelis et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Kavoura and Sabatakakis \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e, Kontoes et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e, Tavoularis et al. \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Nefros et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eA variety of techniques have been established for the implementation of landslide susceptibility, depending on the purpose to which the landslide zoning is to be applied, the scale of assessment or the quality of data (Fell et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Corominas et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). These techniques may be either qualitative or quantitative (Soeters and Van Westen \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e). Quantitative methods are generally regarded as objective, as they produce numerical estimates, whereas qualitative methods are considered to be subjective, based on the expert\\u0026rsquo;s judgment (Reichenbach et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The advantages and limitations of these approaches have been summarized by Aleotti and Chowdhury (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e), Pardeshi et al. (\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), and Reichenbach et al. (\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eSoeters and van Westen (\\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e) suggest that a national scale map is created to give a general overview of problem areas across an entire country, serving to inform national policy makers and the general public. On the other hand, regional scale maps are designed for use by planners in the early phases of regional development projects or engineers evaluating possible constraints in landslide prone areas in the development of large engineering projects and regional development plans. Supporting this view, Corominas et al (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) set the range of \\u0026gt;\\u0026thinsp;1:250,000 for national scale maps and the range of 1:250,000 to 1:25,000 for regional scale analysis.\\u003c/p\\u003e\\u003cp\\u003eLandslide susceptibility mapping relies on a rather complex knowledge of slope movements and their controlling factors. The basic point of view is that landslide susceptibility determines the spatial distribution of landslide predisposing factors such as geological formations, hydrological conditions and topography and identifies zones prone to landslides (Radbruch \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e1970\\u003c/span\\u003e). Selecting the landslide predisposing factors should be taken into account: (i) the scale of the analysis, (ii) the characteristics of the study area, (iii) the landslide type, and (iv) the availability of data at the appropriate scale (van Westen et al. \\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). The reliability of landslide susceptibility maps mainly depends on the quantity and quality of available data, the working scale, and the selection of the appropriate methodology for analysis and modelling (Baeza and Corominas \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe HSGME represents the primary source of landslide information in Greece, providing significant assistance to Civil Protection Authorities. In this context, HSGME investigates and monitor landslides and other ground failures at local, regional and national scale, using a combination of field survey and remote sensing techniques. The aforementioned parameters facilitated the development of the National Landslide Database. However, for many years the company had not published landslide susceptibility maps at any scale. For this reason, the GEOKA project (2018\\u0026ndash;2023) involved studies in the field of natural hazards, focusing among others on landslide phenomena. In the framework of the project, the National Database of Landslides was updated with new records and aligned with European standards as well as data accuracy and integration were also enhanced through field surveys in reactivated landslides. The outcome of the above was a revised inventory map of the landslides that had occurred at the national scale, which will serve as the foundation for further analysis in landslide susceptibility assessment. Hence that the GEOKA project has been designed to fill the gap in research on susceptibility assessment in Greece, with due consideration of the updated National Database of landslides. This paper presents methodology adopted by the project in order to provide landslide susceptibility assessments in regional and national scale. The Analytic Hierarchy Process (AHP) and Frequency Ratio (FR) methods were employed in each study area for six common-used predisposing factors (lithology, slope, land use, distance from faults, distance from hydrographic network and road density). The Landslide Susceptibility Index (LSI) was used for mapping landslide susceptibility level, with validation performed via ROC curves.\\u003c/p\\u003e\"},{\"header\":\"2. Study areas\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.1. Greek territory\\u003c/h2\\u003e\\n \\u003cp\\u003eGreek territory covers an area of approximately 131,000 km\\u003csup\\u003e2\\u003c/sup\\u003e where mountainous regions with altitudes ranging between 600 and 2900 m represent the 33% of the county\\u0026rsquo;s area. It is well known that in Greece several geotectonic zones are distinguished by fundamental differences both in lithology and structure. These geotectonic zones broadly align with the trend of the main mountain ranges (NNW-SSE axis) thus shaping the landscape of the country (Fig. 1).\\u003c/p\\u003e\\n \\u003cp\\u003eThe western and central parts of the country mainly include sedimentary formations not older than Mesozoic associated with E-W compression. The tectonic movements of this period refer to discontinuous westward and eastward orogenic migration, characterized by intense collisional tectonics producing tectonic nappes and slices, which are thrust on top of each other from east to west (Mountrakis et al \\u003cspan class=\\\"CitationRef\\\"\\u003e1983\\u003c/span\\u003e; Doutsos and Kokkalas \\u003cspan class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e; Mantovani et al \\u003cspan class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Sboras et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The eastern part of the country is occupied by metamorphic rocks extended in age from Paleozoic to recent as well as igneous and metamorphic rocks of varying age (Koukis et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). By the end of Alpine orogenesis, post-alpine Neogene sediments were deposited into trenches that have been earlier created by tectonism.\\u003c/p\\u003e\\n \\u003cp\\u003ePrevious studies have explored the relationship between landslides and geological formations in Greece. In 1982, Andronopoulos, investigated some large-scale landslides located in western Greece, thus identified certain factors contributing to instability conditions in upper cretaceous limestones and flysch of Olonos-Pindos zone. According to Koukis et al. (\\u003cspan class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e) this geotectonic zone exhibits the highest frequency of landslide occurrences in Greece, exceeding 40% followed by Pelagonean zone, 39%. Koukis et al. (\\u003cspan class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e) identified that in Western-Central Greece, the highest percentages (60\\u0026ndash;70%) of landslides are recorded in the flysch outcroppings while in Eastern Greece the landslides recorded equal to 80\\u0026ndash;85%, involve Neogene sediments. In addition, Neogene marls and flysch formations are also related on numerous landslides in areas such as Evia Island (Ilia and Tsangaratos \\u003cspan class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) and the northern part of Achaia prefecture (Kavoura and Sabatakakis \\u003cspan class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eIt is worth to note that many researchers have observed that the majority of serious instability phenomena are often detected in the upper weathered zone of flysch and Neogene sediments which have a thickness ranging up to some meters (Koukis et al. 2015; Ilia and Tsangaratos \\u003cspan class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Kavoura and Sabatakakis \\u003cspan class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.2. Pilot areas\\u003c/h2\\u003e\\n \\u003cp\\u003eRegional scale susceptibility assessment was carried out in 4 well-documented landslide prone areas in Greece. In order to evaluate the efficacy of both quantitative and qualitative analysis, the assessments were conducted in pilot areas that differed significantly in terms of size and geoenvironmental settings. Moreover, according to the archive of HSGME, many landslides have been recorded in these areas over many years, frequently affecting residential areas and transportation routes (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The majority of the records refer to rainfall-induced landslides.\\u003c/p\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eDifferences and specifications of selected areas\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePilot areas\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTotal area (km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLandslides\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGeological formations\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCorfu Island\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e590\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e203\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAlpine formations of Ionian Zone \\u0026ndash; Neogene sediments\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePelion province (Magnisia Prefecture)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e905\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e539\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMetamorphic basement of Pelagonian Zone\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEvritania Prefecture\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1870\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e483\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAlpine formations of Olonos-Pindos zone\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAchaia Prefecture\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3275\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e368\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNeogene and Quaternary sediments\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCorfu island\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eCorfu island, with a total size of approximately 590 km\\u003csup\\u003e2\\u003c/sup\\u003e, belongs to the complex of Ionian Islands, in the Ionian Sea in western Greece (Fig. 1). In this area a NNW\\u0026ndash;SSE trending system represents the convergence between the Apulian Platform and the Hellenic foreland, with Corfu lying on the northwestern edge of the Hellenic Fold and Thrust Belt. Additionally, the north part of the island is characterized by a major E\\u0026ndash;W-striking right-lateral structure that crosses Corfu from coast to coast, Southern Salerno\\u0026ndash;North Corfu fault zone, which has resulted in the displacement of N\\u0026ndash;S-trending fold axes and thrusts (Sakkas et al \\u003cspan class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Due to tectonics, the morphological relief is more pronounced in the northern part. Concerning the geology, formations from the Ionian geotectonic zone, mainly limestones, as well as Neogene and Quaternary formations, contribute to the geological structure of Corfu. In general, the geological structure is characterized by intense folded structures, reverse faults, large transverse ruptures and uplifting movements.\\u003c/p\\u003e\\n \\u003cp\\u003eUp to now, little research has been done in the field of landslides in this particular region of the country (Konstantopoulou et al, \\u003cspan class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e, Kavoura et al, \\u003cspan class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). However, updated information reveals a long history of landslides, affecting both residential areas and infrastructures. According to Konstantopoulou et al. (\\u003cspan class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), landslide phenomena in Corfu mainly occur in Neogene sediments, accounting for over 65% of the occurrences while 76% of the recorded landslides are located within residential areas and the road network and the rest of 24% within forests and cultivated lands.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEvritania region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eThe Evritania region is situated in central Greece, encompassing the southern region of the Pindos Mountain range, with an area spanning 1870 km\\u003csup\\u003e2\\u003c/sup\\u003e (Fig. 1). The area\\u0026apos;s tectonic activity is notable, primarily attributed to the presence of the Pindos Mountain range, characterized by extensive folds and successive thrusts. This tectonic activity, coupled with neotectonic processes and lithological factors, has contributed to the development of an intense relief and a complex hydrological network. The geological structure of the region is composed of formations from the Olonos-Pindos geotectonic zone, (flysch, limestone, chert) along with Quaternary formations. Landslide occurrences are predominantly associated with formations of the flysch and they are mostly in the completely weathered zone.\\u003c/p\\u003e\\n \\u003cp\\u003eNumerous studies have been conducted in the Evritania region regarding landslides including landslide susceptibility assessments (Rozos and Apostolidis \\u003cspan class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Marinos et al \\u003cspan class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Atzemoglou et al \\u003cspan class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Ntelis et al \\u003cspan class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Krassakis et al \\u003cspan class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Kontoes et al \\u003cspan class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Investigating landslide susceptibility in a selected part of Evritania region, Atzemoglou et al \\u003cspan class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e reported that several settlements are located within areas characterized by \\u0026ldquo;Very High susceptibility\\u0026rdquo; and the majority of the road network lies in landslide-prone areas. Ntelis et al. (\\u003cspan class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), based on a vague combination of three models (Analytical Hierarchy Process, Frequency Ratio and Fuzzy Logic) concluded that approximately the 45% of the Evritania region is classified in \\u0026ldquo;High\\u0026rdquo; and \\u0026ldquo;Very High\\u0026rdquo; susceptibility zones.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAchaia region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eThe largest of the pilot areas, 3275 km\\u003csup\\u003e2\\u003c/sup\\u003e, Achaia prefecture belongs to the north-western part of Peloponnese peninsula. This area has been characterized as one of the most susceptible to landslides parts in the country (Chalkias et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) where experienced some of the largest as devastating landslides have ever recorded in Greece: Panagopoula landslide (Koukis et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Sabatakakis et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Kavoura et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), Platanos landslide (Tsiambaos et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Kavoura et al. 2020), Karya landslide (Sabatakakis et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Koukis et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Tsiabaos et al. 2015) Tsivlos landslide (Zygouri and Koukouvelas \\u003cspan class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Tichavsk\\u0026yacute; et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). With regard to the geological settings, the landscape evolution in the north, is controlled by the neotectonic action of the graben, which forms the Corinthian Gulf. However, Achaia is also distinguished by a complex geological structure due to the fact that Gavrovo zone formations being covered by overthrusting from the Pindos zone. Therefore, formations from three geotectonic zones (Olonos\\u0026ndash;Pindos, Gavrovo\\u0026ndash;Tripolis and Ionian) participate in the geological structure of Achaia prefecture. More precisely, the bedrock geology comprises mainly carbonates, including limestones, cherts and schists, while a transition zone overlays them, including limestones, shales, cherts and marls, leading to the typical flysch sequence sediments of the Upper Eocene. During the Pliocene and Pleistocene, lacustrine and marine-lacustrine deposits such as marls, sandstones, conglomerates accumulated in the trenches created by the faulting. Subsequently, Quaternary deposits of scree and alluvial fans deposited on the Neogene sediments and flysch formation (Rozos \\u003cspan class=\\\"CitationRef\\\"\\u003e1989\\u003c/span\\u003e; Degnan and Robertson \\u003cspan class=\\\"CitationRef\\\"\\u003e1998\\u003c/span\\u003e; Doutsos et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e; Koukis et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePelion peninsula\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eThe study area extends over 905 km\\u003csup\\u003e2\\u003c/sup\\u003e in Magnisia Prefecture, central Greece in a NW-SE direction between the Aegean Sea to the east and the Pagasetic Gulf to the south. The area of NE Pelion is characterized by steep morphology which contributes to the occurrence of numerous landslide phenomena in the area. The intense morphological relief observed can be attributed to both past geodynamic processes and more recent tectonic movements. It is composed mainly of alpine formations (Galanakis \\u003cspan class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e). Upper Paleozoic-Early Triassic (meta)clastic deposits together with thick Pelagonian carbonates of Triassic and Jurassic age compile the bedrock of the Pelion peninsula (Porkol\\u0026aacute;b et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eAn abundance of technical reports points out that landslides in this area triggered by rainfall caused extensive damages on the road network and buildings. The vast majority, 95%, of landslides occur in soil formations with a relatively minor occurrence, approximately 5%, associated with rock formations (Lekkas et l 1991).\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"3. Material and methods\",\"content\":\"\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.1. Landslide inventory\\u003c/h2\\u003e\\u003cp\\u003eThe reference National Geodatabase of landslides, based on historical archives was the main source of information. The necessity of digitizing the data of the numerous landslide phenomena that have been studied over time by the Hellenic Geological Survey for more than 70 years, was demonstrated as soon as technology made the relevant tools available. The compilation of the database started in 2006 through indexing of the oldest analogue archive reports and their entry into a database. In 2012, Eeckhaut and Herv\\u0026aacute;s pointed out that 2,200 occurrences had been recorded in the National database of landslides. This number would increase in the following years reaching almost 3,000 landslides (Herrera et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Nowadays, the database of Greece summarizes over 5,570 landslides (December 2023). For each landslide record, a range of information is available, including details of the geological conditions, information related to location, the date of occurrence or investigation, the landslide type, the triggering factors, as well as the impact of each landslide, are available. However, this data contains a level of uncertainty about the geospatial accuracy, that depends on the age of the report. Thus, the accuracy of the landslide locations depends on how they are referred to in the historical documents (technical reports). For this reason, information was evaluated by field reconnaissance surveys at the selected regions during the period 2019\\u0026ndash;2023, to increase the accuracy and the reliability of the inventory as well as the landslide susceptibility maps (Fig.\\u0026nbsp;2). In particular, the reconnaissance surveys were conducted with the following objectives i) to examine the landslide activity and potential re-activations, ii) to monitor the evolution of landslides in conjunction with infrastructure, remedial works, residential areas and the physical environment, iii) to verify and correct the geometry previously digitized landslides, iv) to record new landslides in areas without prior information and v) to maintain the landslide database in a current state. Furthermore, additional geospatial assessments were performed randomly throughout the geodatabase to ascertain the accuracy of the final inventory and to address specific instances of interpretation.\\u003c/p\\u003e\\u003cp\\u003eAccording to the above, a multi-temporal landslide inventory of about 5,570 landslides was compiled for conducting a national scale landslide susceptibility assessment. For the purpose of analysis, the available landslide records were transformed into a point layer. Their spatial distribution presents the initial step towards susceptibility assessment (Guzzetti et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). Moreover, regarding the regional scale analysis, four sub-inventories were derived from the national inventory. The total number of landslides in selected areas represent approximately the 27% of the national landslide inventory (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eHowever, a second inventory independent of the main one was mandatory to be generated for each area, under the purpose of verification. Here, the secondary landslide inventories were drawn up as proposed by Remondo et al. (\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). Thus, for all the cases in Greece (Fig.\\u0026nbsp;3a), and Corfu island, the analysis was made using landslides activated in a certain period (before 2020), and the validation was performed by means of landslides that occurred in a different period (after 2020) (Fig.\\u0026nbsp;3b). In all other study areas, the initial inventory was randomly divided into two groups, one for the susceptibility analysis (70% of the total) and one for validation (30% of the total). In particular, these inventories in Achaia, Pelion and Evritania are shown in Figs.\\u0026nbsp;3c, 3d and 3e respectively.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2. Predisposing factors\\u003c/h2\\u003e\\u003cp\\u003eFor the assessment of susceptibility at both national (1:500,000) and regional (1:50,000) scales, six key-role predisposing factors were selected that are often used in such analyses based on literature review and experience in landslide studies (Sabatakakis et al. \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Sakkas et al. \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Malet et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Grozavu \\u0026amp; Patriche \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Balteanu et al. 2010; Lima et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). The preparation of the appropriate thematic layers is an important step in integrating the spatial coverage of the susceptibility of the area under consideration.\\u003c/p\\u003e\\u003cp\\u003eFor the national landslide susceptibility assessment, 100m spatial resolution thematic maps were used, while for the regional scale analyses the resolution was 50m. For this study, six commonly used predisposing factors were chosen to reflect the general geological, topographic and environmental settings in the study areas. Specifically, the following parameters were considered for regional and nationwide susceptibility assessments. In Fig.\\u0026nbsp;4, examples of thematic layers in both scales are presented.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eLithology\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe Geotechnical Map of Greece, at a scale of 1:500,000 (IGME 1993), which classifies the geological formations into 28 geotechnical units, was used in the national scale analyses. At the the regional scale analyses, an adapted map was created at a scale of 1:50,000 scale map was constructed based on the 1:500,000 scale Geotechnical Map of the IGME, the 1:50,000 scale Geological Sheets of the IGME, data from fieldwork and the parallel use of satellite imagery.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eSlope angle\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe slope was derived from the slope map as created by the Digital Surface Model (DSM) of Hellenic Cadastral S.A. with a resolution of 5X5 m. The final slope map was adjusted to a resolution of 50m at both resolution scales, in order to ensure the rendering of the majority of the slope classes. The classification is performed in 6 classes.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eLand use\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe classification of land use was based on the Corine Land Cover 2018 plan, which is part of the Pan-European Land Use Database. This project includes the mapping of land use/land cover of various European countries (including Greece) at a scale of 1:100,000. For Greece 42 land use categories have been identified.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eRoad network\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThis factor used as an indicator of the impact of the road network on landslide occurrence. The choice of density instead of distance from the road axis is expected to give more objective results on the effect of the factor, while isolating densely populated areas where both the occurrence of events is not expected and extensive man-made interventions may have been implemented. The road network used was derived from the OpenStreetMap (OSM) open data (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.openstreetmap.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.openstreetmap.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). OSM as an information source is suitable for use by public authorities due to its free use of unrestricted data, its completeness and its interoperability stems from users.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFaults\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe presence of tectonic structures (faults, thrusts, folds) affects the mechanical characteristics of the rock mass and geological formations in the area where they are located. For this reason, and given the scale of the susceptibility analysis, the traces of tectonic faults (faults, thrusts etc) as recorded in the Geological Sheets of IGME were used. This digital thematic layer in vector format was provided by the Department of General Geology, Geological Mapping and Applications of HSGME. A 100m resolution and 1000m radius of influence tectonic structure density map was created for the national scale analyses. For the regional scale analyses, the concept of distance from faults was used, which included buffer zones of a 50m constant step.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eHydrographic network\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eRivers and the erosion of their slopes is one of the most common factors causing landslides, especially in areas with intense morphological relief and a dense hydrographic network with deep valleys. Two different hydrographic network backgrounds at different scales were provided by the Department of Hydrogeology and Hydrology of HSGME. The hydrographic network at a scale of 1:250,000 was used for the national scale analyses while for the regional scale analyses employed the network mapping at the water district level was used. Similar to the factor of tectonic structures, a density map and influence zone map were used respectively.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.3. Methods\\u003c/h2\\u003e\\u003cp\\u003eThe methodology followed in the current work is the comparison and the evaluation of the landslide susceptibility that derived from a statistical method (quantitative analysis) and a qualitative expert-based method. The adopted methodology was applied to model landslide susceptibility separately for each area and both methods to account for the differences in the relative contribution of the landslide causative factors. The landslide susceptibility assessment was carried out with Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) method. The overall susceptibility of an area was determined by synthesizing all the factors using an algebraic approach, resulting in a Landslide Susceptibility Index (LSI) used to map the level of landslide susceptibility. Finally, the validation and the evaluation of results achieved in the previous stage were conducted by ROC curves.\\u003c/p\\u003e\\u003cp\\u003eThe AHP was used to derive factor weights and factor class weights from a pairwise comparison matrix, based on normalized landslide frequency ratios and expert knowledge adjustments. The application of the Analytical Hierarchy Process (AHP) method, developed by Saaty (\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e1977\\u003c/span\\u003e), has been used by many authors worldwide, as a multi-criteria decision-making method. It involves making binary comparisons of factors within a complex problem.\\u003c/p\\u003e\\u003cp\\u003eAfter constructing a hierarchical representation of the problem, the next steps involve pairwise comparisons of factors and subfactors using a nine-point scale in a matrix table. The scale values range from 1 (equal importance) to 9 (extremely stronger importance), with intermediate values such as 2, 4, 6, and 8 indicating intermediate levels of importance (Saaty, \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e1977\\u003c/span\\u003e). Each factor is assessed in relation to every other factor using values from 1/9 to 9. Subsequently, the relative weights for each factor and subfactor in the decision hierarchy are estimated.\\u003c/p\\u003e\\u003cp\\u003eThe consistency ratio (CR) is then calculated to validate the AHP results and prevent arbitrary choices in the matrix. The CR is considered valid if it is equal to or less than 0.1 (10%) (Saaty, \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e1978\\u003c/span\\u003e). The equation (Eq.\\u0026nbsp;1) for calculating the consistency ratio is:\\u003c/p\\u003e\\u003cp\\u003eCR = (CI/RI)*100 (Eq.\\u0026nbsp;1)\\u003c/p\\u003e\\u003cp\\u003ewhere RI is the random consistency index and CI is the average consistency index calculated as (Eq.\\u0026nbsp;2):\\u003c/p\\u003e\\u003cp\\u003eCI = (λ\\u003csub\\u003emax\\u003c/sub\\u003e \\u0026ndash; n)/(n \\u0026ndash; 1) (Eq.\\u0026nbsp;2)\\u003c/p\\u003e\\u003cp\\u003ewhere λ\\u003csub\\u003emax\\u003c/sub\\u003e is the maximum eigenvalue of the comparison matrix, and n is the number of factors.\\u003c/p\\u003e\\u003cp\\u003eThe Frequency Ratio (FR) model, as a statistical approach, based on the analysis between distribution of landslides and each landslide-related factor, to reveal the correlation between landslide locations and the factors in a specific area (Lee and Pradhan \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). Therefore, the frequency ratios of each factor class were calculated from their relationship with landslide events. According to the method, the number of landslides in each class is evaluated and the frequency ratio for each factor class is found by dividing the landslide ratio by the area ratio (Lee and Talib \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). If the ratio (FR) is greater than 1, then the relationship between a landslide and the factor\\u0026rsquo;s class is strong while if ratio is less than 1, the relationship is weak.\\u003c/p\\u003e\\u003cp\\u003eThe Frequency Ratio model (FR) is widely used in the international literature (e.g. Lee and Tallib, 2005; Pradhan and Youssef, \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Regmi et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Kavoura et al. 2020) due to its simple application in cases where a comprehensive multi-factor landslide database is available.\\u003c/p\\u003e\\u003cp\\u003eThe validation process is a crucial stage in developing landslide susceptibility models. One of the most widely used quantitative validation methods is the Receiver Operating Characteristic curves (ROC curves). These curves can be used to test the success of the model or the prediction ability. Success rate curves are plotted taking into account the landslides themselves that were used to develop the model (training set). On the other hand, prediction rate curves are built considering independent landslides (validation set) and measure the prediction skill of the model. Many researchers use this method for the evaluation of susceptibility models (Van Den Eeckhaut et al. \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Akgun \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Schicker and Moon \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Ciurleo et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"4. Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.1. Landside susceptibility assessment in Greece\\u003c/h2\\u003e\\u003cp\\u003eUtilizing AHP in the study areas, the calculation commenced with pairwise comparisons of all possible pairs of factors in a matrix based on expert knowledge. Subsequently, values and weights were determined, and in the final step, the consistency index (CI) and consistency ratio (CR) were calculated. The procedure was repeated until the CR\\u0026thinsp;\\u0026le;\\u0026thinsp;0.1 (10%). In contrast to the AHP method, the FR method requires a training data set to compute the weights for each factor and its classes. Training sets as well as test sets were generated as described above. The FR model was applied to define weights for each factor, using the ratio of the percentage of landslides in a class of the selected factor to the percentage of the area of this class in the study area. Based on the results of the hierarchy process analysis and FR model (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) regional scale susceptibility maps were produced according to LSI calculations. Likewise, the procedure also was run for national scale estimations as presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003eAs regards the landslide susceptibility maps were produced in regional scale the classification of the susceptibility level is given in a percentage scale of 10%, so that each class differs from the next in terms of the degree of susceptibility, i.e. the probability of a landslide occurring in the area according to the conditions of the area, by 10% (Fig.\\u0026nbsp;5). The lower the percentage the lower the susceptibility level. This classification would be favour to simplify the legibility and the comparison between the landslide susceptibility maps. However, in national scale maps the degree of susceptibility is classified into 5 classes according to the natural breaks method (Fig.\\u0026nbsp;6). The distribution of the data is done in such a way that the average value of each value interval is closest to the values of that interval. This ensures that the value intervals are best represented by their averages and that the data values between these intervals are reasonably close. In this case the susceptibility is described as Very Low, Low, Medium, High and Very High.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eModified factors of faults and streams for national scale assessments\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFactor\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSub-factors\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eWeight\\u003csub\\u003eAHP\\u003c/sub\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eFR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eCR\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e\\u003cp\\u003eFaults density\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u0026ndash;0,5 km/km\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.082\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e\\u003cp\\u003e0.068\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0,5\\u0026thinsp;\\u0026minus;\\u0026thinsp;1 km/km\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.117\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u0026ndash;2 km/km\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.126\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2\\u0026ndash;3 km/km\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.221\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u0026ndash;4 km/km\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.234\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4\\u0026ndash;5 km/km\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.221\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e\\u003cp\\u003eDensity of hydrographic 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colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.172\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.245\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.137\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e1.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.322\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.25\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e46\\u003csup\\u003eο\\u003c/sup\\u003e-60\\u003csup\\u003eο\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.293\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.327\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.175\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.89\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.236\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e2.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.217\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.89\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;60\\u003csup\\u003eo\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.124\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.110\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.114\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.45\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.459\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.133\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.45\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"27\\\" rowspan=\\\"28\\\"\\u003e\\u003cp\\u003eLithology\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eQuaternary loose fine-grained deposits with organics\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"27\\\" rowspan=\\\"28\\\"\\u003e\\u003cp\\u003e0.099\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"27\\\" rowspan=\\\"28\\\"\\u003e\\u003cp\\u003e0.081\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\" morerows=\\\"27\\\" rowspan=\\\"28\\\"\\u003e\\u003cp\\u003e0.094\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c14\\\" morerows=\\\"27\\\" rowspan=\\\"28\\\"\\u003e\\u003cp\\u003e0.064\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\" morerows=\\\"27\\\" rowspan=\\\"28\\\"\\u003e\\u003cp\\u003e0.098\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eQuaternary loose deposits, coarse-grained sediments\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.072\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e3.61\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.042\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.23\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.015\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eQuaternary loose deposits, fine-grained sediments\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.030\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.015\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eQuaternary loose deposits of mixed phases\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.080\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.48\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.057\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.063\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.066\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0,4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eQuaternary coherent coarse-grained deposits\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.105\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.063\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.055\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.045\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e2.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eQuaternary coherent deposits of mixed phases\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.206\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.031\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.020\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNeogene coarse-grained sediments\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.073\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.014\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNeogene fine-grained sediments\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.172\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.151\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e4.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.059\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e2.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNeogene sediments of mixed phases\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.266\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.129\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.26\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.088\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eThrace molasse mainly with fine-grained sediments\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.014\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eThrace molasse sediments of mixed phases\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMolasse deposits of Mesohellenic Trough\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.012\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFlysch (siltstones and sandstones)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.072\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.359\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.142\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.75\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.054\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.143\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e2.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFlysch (conglomerate and sandstones)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.117\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.53\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.040\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.019\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLimestones\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.047\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.033\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.041\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.045\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.029\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLimestones with silex nodule and phacoids\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.190\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.55\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.031\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.32\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.081\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLimestones with chert, schist-chert or schist-marl\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.070\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.029\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e6.76\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.020\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLimestones-Dolimitic limestones-Dolomites\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.036\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.050\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.042\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eClayey shale and chert\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.059\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2.94\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.026\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.029\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCherts with marly lists\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGypsum or/and breccia (calcareous-dolomitic)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.014\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.030\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.015\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSemi-metamorphic rocks\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.035\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.189\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.23\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.057\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMetamorphic carbonate rocks\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.151\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.038\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMetamorphic rocks\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.281\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e2.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.083\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAcid plutonic rocks\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMafic-Ultramafic Igneous rocks\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.041\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.013\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eVolcanic rocks (lava)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eVolcanic rocks (lava)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.033\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"40\\\" rowspan=\\\"41\\\"\\u003e\\u003cp\\u003eLand use\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eContinuous urban fabric\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.017\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"40\\\" rowspan=\\\"41\\\"\\u003e\\u003cp\\u003e0.099\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"40\\\" rowspan=\\\"41\\\"\\u003e\\u003cp\\u003e0.103\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.013\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\" morerows=\\\"40\\\" rowspan=\\\"41\\\"\\u003e\\u003cp\\u003e0.089\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.015\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e2.93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c14\\\" morerows=\\\"40\\\" rowspan=\\\"41\\\"\\u003e\\u003cp\\u003e0.055\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDiscontinuous urban fabric\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.073\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.039\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2.93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.026\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e3.48\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.095\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.022\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eIndustrial or commercial units\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.017\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.016\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePermanently irrigated land\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.034\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.024\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.022\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0,1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eRice fields\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.015\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eVineyards\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.028\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.059\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e9,77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.036\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFruit trees and berry plantations\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.040\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0,69\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.094\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.035\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\" morerows=\\\"25\\\" rowspan=\\\"26\\\"\\u003e\\u003cp\\u003e0.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eOlive groves\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.081\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.060\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.054\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e4,03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.086\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.059\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePastures\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.035\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.097\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.033\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.038\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAnnual crops associated with permanent crops\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eComplex cultivation patterns\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.097\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.046\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.066\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e3.94\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.067\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.056\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLand principally occupied by agriculture, with significant areas of natural vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.104\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.137\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e6.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.086\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e8.87\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.087\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.064\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAgro-forestry areas\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.048\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eBroad-leaved forest\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.046\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.067\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.32\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.037\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e5.74\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.064\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e6.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.037\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eConiferous forest\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.071\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.38\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.042\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.054\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.031\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMixed forest\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.075\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.031\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.055\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.024\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNatural grasslands\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.027\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.034\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.032\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.46\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.030\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e1.81\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMoors and heathland\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.040\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.08\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.036\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSclerophyllous vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.048\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.062\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.13\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.048\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.038\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e10.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.037\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTransitional woodland-shrub\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.025\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.039\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.029\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.037\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.024\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eBeaches, dunes, sands\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.046\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.029\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.025\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.022\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eBare rocks\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.030\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.026\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.021\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSparsely vegetated areas\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.029\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.029\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.38\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.034\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.035\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.025\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eInland marshes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.013\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eBurnt areas\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.035\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSalt marshes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.009\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSalines\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd 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colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.040\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u003cp\\u003e0.031\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u003cp\\u003e0.38\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003eDistance from streams\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0-50m\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.444\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003e0.006\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.388\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003e0.048\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.381\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003e0.061\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.419\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c14\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003e0.019\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e51-100m\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.262\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.268\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.08\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.274\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.04\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.250\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e101-150m\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.153\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.41\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.167\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.166\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.28\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.163\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e151-200m\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.089\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.38\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.097\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.94\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.113\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e1.25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e0.55\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;200m\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.053\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.080\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.91\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.065\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e0.95\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.067\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e1.71\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\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\\u003e4.2. Models evaluation\\u003c/h2\\u003e\\u003cp\\u003eAn important stage in landslide susceptibility assessment is to evaluate the effectiveness of the produced landslide susceptibility map. For this purpose, receiver operating characteristic (ROC) curves were used, firstly for checking the reliability of the proposed model (success rate curves) as well as to check the ability of the model to pinpoint landslide-prone areas (prediction rate curves).\\u003c/p\\u003e\\u003cp\\u003eThe accuracy of the model is checked for each area, using the training set and an equal number random set of points free of landslides. The process was repeated for the validation set of landslides in order to find if these independent landslide occurrences were correctly adapted in different susceptible areas.\\u003c/p\\u003e\\u003cp\\u003eBased on these, the results of AHP and FR methods were compared in order to achieve the accuracy level of each susceptibility model. From the graphs of Fig.\\u0026nbsp;6, show that FR model has better performance than AHP in all cases of correlations (Fig.\\u0026nbsp;7). High performance is actually recognized in both success and prediction checks.\\u003c/p\\u003e\\u003cp\\u003eThe results, as shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, indicate that in Corfu island both models have the ability to correctly predict the susceptible areas, with the FR model being more successful while AUC value is approximately 0.9. Similar results are noted in Pelion where both methods give AUC\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.7, with the FR model performing better. The susceptibility models in Evritania, the AHP method does not correctly assign the degree of susceptibility, not being able to accurately classify the already recorded landslides. On the other hand, the FR method is considered efficient as AUC\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.8. Such statistical performance was also observed in Achaia. Finally, in the case on Greek territory a good predictive accuracy was also obtained for the FR method, with AUC\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.8 in contrast with the results of AHP method. In addition, further analysis on AUC values between success and prediction rates were conducted. Thus, the percentage difference comparison was applied on AUC values of each validation method. The percentage difference was calculated between two number values, AUC\\u003csub\\u003esuccess\\u003c/sub\\u003e and AUC\\u003csub\\u003epredict\\u003c/sub\\u003e in order to determine how close they are. A high percent difference indicates a large relative change between the values, while a lower percent difference suggests a smaller relative change.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eROC analysis results for success and prediction rates\\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=\\\"char\\\" char=\\\".\\\" 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\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eStudy area\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eAUC\\u003csub\\u003eAHP\\u003c/sub\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eDifference %\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003eAUC\\u003csub\\u003eFR\\u003c/sub\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eDifference %\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSuccess\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePrediction\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eSuccess\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003ePrediction\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCorfu island\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0,747\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0,706\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5,6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0,893\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0,906\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1,4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEvritania\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0,499\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0,429\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e15,1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0,872\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0,874\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0,2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAchaia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0,614\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0,558\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e9,6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0,864\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0,826\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4,5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePelion\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0,771\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0,745\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3,4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0,838\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0,879\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4,8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGreece\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0,673\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0,689\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0,871\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0,854\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2,0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"5. Discussion\",\"content\":\"\\u003cp\\u003eLosses and damages associated with landslides can be reduced significantly if decision makers of all levels of government take well-informed actions before a disaster occurs and respond appropriately after a disaster. Landslide susceptibility assessment keeps a fundamental role in landslide risk management. From this point of view, national, regional and local scale landslide susceptibility maps are going to be a very important tool for decision-makers such as civil protection authorities and stakeholders. This study based on the updated National Landslide Database of H.S.G.M.E. proceeds to estimate the effectiveness of specific areas in landslides, through modelling and compilation of reliable and accurate susceptibility maps. As result, landslide susceptibility assessments at national (1:500,000) and regional scale (1:50,000) were contacted for Greece. Comparing the results of statistical and knowledge-based methods were applied for landslide susceptibility assessment, a new national-scale susceptibility map is presented. In addition, regional scale analyses also were discussed regarding four well-known landslide prone regions in Greece.\\u003c/p\\u003e\\u003cp\\u003eAccording to the ROC analysis, FR model seems to have better performance than AHP model. Whereas the area under curve (AUC) value tends to be higher with respect to unity (1), the accuracy of the model increased. Figure\\u0026nbsp;8 compares the results obtained from ROC analysis to verify the ability of the model to correctly classify landslides into susceptibility zones (success rate) with those that verify ability of the model to pinpoint landslide-prone areas (prediction rate). Interestingly, the relationship between success and prediction values has a positive correlation for both models (AHP and FR). However, the FR dataset displays a stronger relationship between success and prediction values than AHP with AUC value stays above 80% in any case. These results further support the idea that FR model provides sufficient landslide susceptibility mapping in regional and national scale.\\u003c/p\\u003e\\u003cp\\u003eAnother important finding is that the calculated weights for every class of each factor can describe the most landslide-prone areas by suggesting crucial combinations of factors occur in specific regions. It is important to mention that these factors are strongly connected with the mapping accuracy of thematic layers. In Greece, the flysch formation seems to be the most susceptible to landslides, which is in agreement with previous studies (Koukis et al \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Sabatakakis eta al. 2012; Sakkas et al \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eLandslide susceptibility assessment in the island of Corfu highlights the impact of landslide hazard in highly residential and touristic areas. Apart from the statistical checks of the model\\u0026rsquo;s prediction accuracy, in Corfu island, a retrospective evaluation of landslide susceptibility maps was evaluated based on a landslide event triggered by heavy rainfalls in the year 2022 (Corominas et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Fleuchaus et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). On 11 to 12 December 2022 an extreme rainfall event hit the north-western part of Greece including the island of Corfu. These days numerous landslides were activated at Chlomos and Agios Dimitrios villages, at southern eastern part of island. Comparing landside locations with the susceptibility map from the FR method will see that they were activated in areas with very high susceptibility level over 70%, despite the fact that the scale is 1:50000. Lower level of accuracy indicates the AHP model (Fig.\\u0026nbsp;9).\\u003c/p\\u003e\\u003cp\\u003eWhile Hellenic Survey of Geology and Mineral Exploration HSGME serves the public and the authorities by providing reliable scientific information and thus minimizing loss of life and property from natural disasters, this research could be a basic tool for managing a sustainable hazard and risk mitigation program in landslide prone area.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was conducted in the framework of the Operational Program entitled \\u0026quot;Competitiveness, Entrepreneurship and Innovation (2015-2020), Project \\u0026laquo;Studies and researches support to the energy sector, industry and entrepreneurship\\u0026raquo;, Sub-Project \\u0026laquo;Susceptibility assessment of landslides in the Greek territory - Volcanic study and risk assessment\\u0026raquo;, financed by the European Regional Development Fund.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval:\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e:\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no conflict of interest.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAkgun \\u0026Alpha; (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey. 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Estud Geol 65(1):49\\u0026ndash;65. https://doi.org/10.3989/egeol.08642.036\\u003c/li\\u003e\\n\\u003cli\\u003eChalkias C, Kalogirou S, Ferentinou M (2014) Landslide susceptibility, Peloponnese Peninsula in South Greece, Journal of Maps, 10:2, 211-222, DOI:10.1080/17445647.2014.884022\\u003c/li\\u003e\\n\\u003cli\\u003eCiurleo M, Calvello M, Cascini L (2016) Susceptibility zoning of shallow landslides in f ine grained soils by statistical methods. Catena 139:250\\u0026ndash;264.\\u003c/li\\u003e\\n\\u003cli\\u003eCorominas J, Van Westen C, Frattini P, Cascini L, Malet J-P, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi S, Tofani V, Herv\\u0026aacute;s J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. 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SEC (2010) 1626 final, 21.12.2010, Brussels, Belgium\\u003c/li\\u003e\\n\\u003cli\\u003eEeckhaut MVD, Herv\\u0026aacute;s J (2012) State of the art of national landslide databases in Europe and their potential for assessing landslide susceptibility, hazard and risk. Geomorphology, 139\\u0026ndash;140: 545-558. https://doi.org/10.1016/j.geomorph.2011.12.006\\u003c/li\\u003e\\n\\u003cli\\u003eFell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Engineering Geology, 102: 85\\u0026ndash;98. doi: 10.1016/j.enggeo.2008.03.022\\u003c/li\\u003e\\n\\u003cli\\u003eFerentinou M, Chalkias C (2013) Mapping mass movement susceptibility across Greece with gis, ann and statistical methods. 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Landslides 15:359\\u0026ndash;379. https://doi.org/10.1007/s10346-017-0902-z\\u003c/li\\u003e\\n\\u003cli\\u003eHerv\\u0026aacute;s J, G\\u0026uuml;nther A, Reichenbach P, Chac\\u0026oacute;n J, Pasuto A, Malet J-P, Trigila A, Hobbs P, Maquaire O, Tagliavini F, Poyiadji E, Guerrieri L, Montanarella L (2007) Recommendations on a common approach for mapping areas at risk of landslides in Europe. In: Herv\\u0026aacute;s J (ed), Guidelines for mapping areas at risk of landslides in Europe. Proceedings Experts Meeting, Ispra, Italy, 23\\u0026ndash;24 October 2007. JRC Report EUR 23093 EN. Office for Official Publications of the European Communities, Luxembourg\\u003c/li\\u003e\\n\\u003cli\\u003eIlia I, Tsangaratos P (2016) Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. 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Geosciences, 12:104. https://doi.org/10.3390/ geosciences12030104\\u003c/li\\u003e\\n\\u003cli\\u003eMarinos V, Papathanassiou G, Vougiouka E, Karantanellis E (2015) Towards the Evaluation of Landslide Hazard in the Mountainous Area of Evritania, Central Greece. In: Lollino, G., et al. Engineering Geology for Society and Territory - Volume 2. Springer, Cham. https://doi.org/10.1007/978-3-319-09057-3_173\\u003c/li\\u003e\\n\\u003cli\\u003eMountrakis D, Sapountzis E, Kilias A, Eleftheriadis G, Christofides G (1983) Paleogeographic conditions in the western Pelagonian margin in Greece during the initial rifting of the continental area. Canadian Journal of Earth Sciences, 20 (11):1673 \\u0026ndash; 1681. DOI: 10.1139/e83-158\\u003c/li\\u003e\\n\\u003cli\\u003eNefros C, Tsagkas DS, Kitsara, G, Loupasakis C, Giannakopoulos C (2023) Landslide Susceptibility Mapping under the Climate Change Impact in the Chania Regional Unit, West Crete, Greece. 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US Geological Survey Open-File Report 85\\u0026ndash;585\\u003c/li\\u003e\\n\\u003cli\\u003eRegmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7:725\\u0026ndash;742. https://doi.org/10.1007/s12517-012-0807-z\\u003c/li\\u003e\\n\\u003cli\\u003eReichenbach P, Rossi M, Malamud B, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180: 60-91\\u003c/li\\u003e\\n\\u003cli\\u003eRemondo J, Gonz\\u0026aacute;lez A, D\\u0026iacute;az de Ter\\u0026aacute;n JR, Cendrero A, Fabbri A, Chung CJF (2003) Validation of landslide susceptibility maps; examples and applications from a case study in northern Spain. Natural Hazards, 30 (3): 437-449. DOI: 10.1023/B:NHAZ.0000007201.80743.fc\\u003c/li\\u003e\\n\\u003cli\\u003eRozos D (1989) Engineering-geological conditions in the Achaia County. Geomechanical characteristics of the Plio-pleistocene sediments. PhD thesis, University of Patras, Greece, pp 453 (In Greek)\\u003c/li\\u003e\\n\\u003cli\\u003eRozos D, Apostolidis E (2004) Engineering geological investigation of slope failures in Paleo Mikrohorio Evritania Pr., aiming at its safe residential development. Bulletin of the Geological Society of Greece, 36(4):1806\\u0026ndash;1815. https://doi.org/10.12681/bgsg.16651 (In Greek)\\u003c/li\\u003e\\n\\u003cli\\u003eSaaty T (1977) A Scaling Method for Priorities in Hierarchical Structures, Journal of Mathematical Psychology, 15:234-281.\\u003c/li\\u003e\\n\\u003cli\\u003eSaaty T (1978) Modeling Unstructured Decision Problems-The Theory of Analytical Hierarchies Mathematics and Computers in Simulation. 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Transportation Research Board, National Research Council, Special Report, 247: 129-177\\u003c/li\\u003e\\n\\u003cli\\u003eTavoularis N, Papathanassiou G, Ganas A, Argyrakis P (2021) Development of the Landslide Susceptibility Map of Attica Region, Greece, Based on the Method of Rock Engineering System. Land. 10(2):148. https://doi.org/10.3390/land10020148\\u003c/li\\u003e\\n\\u003cli\\u003eTichavsk\\u0026yacute; R, Fabi\\u0026aacute;nov\\u0026aacute; A, Koutroulis A, Sp\\u0026aacute;lovsk\\u0026yacute; V, Vala O (2023) Recent debris-flow activity on the 1913 Tsivlos landslide body (Northern Peloponnese; Greece), CATENA, 231. https://doi.org/10.1016/j.catena.2023.107318\\u003c/li\\u003e\\n\\u003cli\\u003eTrigila A, Frattini P, Casagli N, Catani F, Crosta G, Esposito C, Iadanza C, Lagomarsino D, Scarascia G, Segoni S, Spizzichino D, Tofani V, Lari S (2013) Landslide susceptibility mapping at national scale, the Italian case study. In: Margottini C, Canuti P, Sassa K (Eds.), Landslides Science and Practice. Vol. 1. Springer, Berlin,Heidelberg, pp. 287\\u0026ndash;295\\u003c/li\\u003e\\n\\u003cli\\u003eTsiambaos G, Sabatakakis N, Rondoyanni Th, Depoundis N, Kavoura K (2015) Composite landslides affecting flysch and Neogene weak rock formations induced by heavy rainfalls. 13th ISRM Congress Proceedings - Int\\u0026rsquo;l Symposium on Rock Mechanics - Innovations in Applied and Theoretical Rock Mechanics. ISBN: 978-1-926872-25-4, p651, 10p\\u003c/li\\u003e\\n\\u003cli\\u003eVan Den Eeckhaut M, Reichenbach P, Guzzetti F, Rossi M, Poesen J (2009) Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Nat Hazards Earth Syst Sci 9:507\\u0026ndash;521\\u003c/li\\u003e\\n\\u003cli\\u003evan Westen CJ, Castellanos E, Kuriakose S (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview, Engineering Geology, 102(3\\u0026ndash;4): 112-131. https://doi.org/10.1016/j.enggeo.2008.03.010\\u003c/li\\u003e\\n\\u003cli\\u003eWilde M, G\\u0026uuml;nther A, Reichenbach P, Malet J P, Herv\\u0026aacute;s J (2018) Pan-European landslide susceptibility mapping: ELSUS Version 2. Journal of Maps, 14(2):97\\u0026ndash;104. https://doi.org/10.1080/17445647.2018.1432511\\u003c/li\\u003e\\n\\u003cli\\u003eZygouri V, Koukouvelas IK (2019) Landslides and natural dams in the Krathis River, north Peloponnese, Greece. Bull Eng Geol Environ 78:207\\u0026ndash;222. https://doi.org/10.1007/s10064-017-1225-yz\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"natural-hazards\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"nhaz\",\"sideBox\":\"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)\",\"snPcode\":\"11069\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11069/3\",\"title\":\"Natural Hazards\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Landslide inventory, FR, AHP, ROC curves, landslide hazard, multi-scales, predisposing factors, national database of landslides\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4838383/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4838383/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe current work figures out an approach for estimating landslide susceptibility in Greece. This is the first study to undertake the updated official National Geodatabase of Landslides, which encompasses the entire country for the purpose of analyzing landslide susceptibility. This research acknowledges the critical role that scale plays in landslide susceptibility modelling. From this perspective, regional scale analyses conducted in selected areas along Greece, to gain a deeper understanding of the challenges encountered by the process in ensuring the results of national scale assessment after that. The methodology followed in the current work is the comparison and evaluation of the landslide susceptibility that derives from a statistical method (quantitative analysis) and an expert-based method (qualitative). Afterwords, this research focuses on the evaluation of the results in any scale and suggests a framework for working on landslides susceptibility assessment in Greece.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Landslide Susceptibility Assessment in Greece: work in regional and national scale\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-08-27 16:46:43\",\"doi\":\"10.21203/rs.3.rs-4838383/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2025-08-19T11:54:25+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-08-19T11:15:14+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-08-02T05:06:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Natural Hazards\",\"date\":\"2024-08-01T03:21:46+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"natural-hazards\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"nhaz\",\"sideBox\":\"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)\",\"snPcode\":\"11069\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11069/3\",\"title\":\"Natural Hazards\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"f51ec3e2-7293-4757-83e7-3b66a9dba26e\",\"owner\":[],\"postedDate\":\"August 27th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-16T21:46:15+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-08-27 16:46:43\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4838383\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4838383\",\"identity\":\"rs-4838383\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}