Landslide susceptibility assessment in the İzmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method

Kıncal, Cem; Akgün, Aykut; Koca, Mehmet Yalçın

doi:10.1007/s12665-009-0070-0

Cited by 50 publications

(17 citation statements)

References 52 publications

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“…Topographic indices such as SPI and TWI are used to describe spatial soil moisture patterns [27,28]. Lithology plays an important role in landslide occurrences because different lithologic units have varied inherent characteristics, including strength, composition, and structure, producing varied resistance against landslides [45][46][47]. Fault lines are the expression of structural brittle deformation of rocks due to tectonics, and thus landslide susceptibility is higher along these features.…”

Section: Datamentioning

confidence: 99%

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

2017

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Abstract:In this study, the support vector machine (SVM) was applied and validated by using the geographic information system (GIS) in order to map landslide susceptibility. In order to test the usefulness and effectiveness of the SVM, two study areas were carefully selected: the PyeongChang and Inje areas of Gangwon Province, Korea. This is because, not only did many landslides (2098 in PyeongChang and 2580 in Inje) occur in 2006 as a result of heavy rainfall, but the 2018 Winter Olympics will be held in these areas. A variety of spatial data, including landslides, geology, topography, forest, soil, and land cover, were identified and collected in the study areas. Following this, the spatial data were compiled in a GIS-based database through the use of aerial photographs. Using this database, 18 factors relating to topography, geology, soil, forest and land use, were extracted and applied to the SVM. Next, the detected landslide data were randomly divided into two sets; one for training and the other for validation of the model. Furthermore, a SVM, specifically a type of data-mining classification model, was applied by using radial basis function kernels. Finally, the estimated landslide susceptibility maps were validated. In order to validate the maps, sensitivity analyses were carried out through area-under-the-curve analysis. The achieved accuracies from the SVM were approximately 81.36% and 77.49% in the PyeongChang and Inje areas, respectively. Moreover, a sensitivity assessment of the factors was performed. It was found that all of the factors, except for soil topography, soil drainage, soil material, soil texture, timber diameter, timber age, and timber density for the PyeongChang area, and timber diameter, timber age, and timber density for the Inje area, had relatively positive effects on the landslide susceptibility maps. These results indicate that SVMs can be useful and effective for landslide susceptibility analysis.

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Section: Datamentioning

confidence: 99%

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

2017

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“…SCAI shows the density of landslides among the landslide susceptibility zones. It is expected that the high and very high susceptibility classes should have very small SCAI values and low, very low susceptibility zones should have higher SCAI values (Süzen and Doyuran 2004;Kıncal et al 2009). …”

Section: Scai Indexmentioning

confidence: 99%

Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland

Pawłuszek

Borkowski

2016

Nat Hazards

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Choosing appropriate landslide-controlling factors (LCFs) in landslide susceptibility mapping (LSM) is a challenging task and depends on the nature of terrain and expert knowledge and experience. Nowadays, it is very common to use digital elevation model (DEM) and DEM-derivatives, as a representation of the topographic conditions. The objective of this study is to explore topography in depth and simultaneously reduce redundant information within DEM-derivatives using principal component analysis. Moreover, this study investigates the impact of DEM-derived factors on LSM. Therefore, three various strategies were tested. The first strategy included a set of LCFs created from the four initial principal components, which were provided from DEM-derived factors. The second strategy included a set of parameters which contained additional lithological and environmental factors. The third strategy utilises the analytical hierarchy process (AHP) to assign weights to each LCF. The LSM was performed based on landslide susceptibility index. Obtained results show that 60% of existing landslides fell into high and very high susceptibility zones using first and second strategies. It proves that topographic factors play a significant role in LSM. Adding additional lithological and environmental factors to the set of LCFs did not improve the results significantly, unless the AHP was used in the third strategy. It improved results significantly; up to 70%. Results from second and third strategies highlight utility of AHP in LSM. Presented studies were performed on the area very prone to landslide occurrence in the region of Ro_ znów Lake, Poland.

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“…The seed cell area index (SCAI) method proposed by Suzen and Doyuran (2004b) was applied. The logic behind SCAI lies in the correct classification of seed cells within a very conservative areal extent, and it is expected that the high and very high susceptibility classes should have very small SCAI values and low and very low susceptibility classes will have higher SCAI values (Kıncal et al 2009). In this context, the susceptibility area percent values are divided by the landslide seed cell percent values to develop the seed cell area index density of landslides among the classes, which is presented in Table 4.…”

Section: Validation Of the Produced Susceptibility Mapmentioning

confidence: 99%

“…Combinations of landslide-conditioning parameter maps, bivariate statistical methods, multivariate statistical methods, and knowledge-based techniques, such as artificial neural networks and fuzzy logic approaches, form the statistical and landslide inventory-based probabilistic methods for landslide susceptibility mapping (Chacon et al 2006). There are many examples of the combinations of landslidecontrolling parameters by using the (Gokceoglu and Aksoy 1996;Donati and Turrini 2002) bivariate statistical methods (Suzen and Doyuran 2004a;Yalcin 2008), multivariate statistical methods (Carrara 1983;Clerici et al 2002;Ohlmacher and Davis 2003;Suzen and Doyuran 2004b;Can et al 2005;Akgun and Bulut 2007;Nefeslioglu et al 2008;Kıncal et al 2009), knowledge-based methods (Ercanoglu and Gokceoglu 2002;Ermini et al 2005;Lee et al 2006;Yesilnacar and Topal 2005), and landslide inventory-based probabilistic methods (Lee 2005;Lee and Pradhan 2007;Akgun et al 2008) in the literature. The aim of this study is to ascertain the landslide susceptibility of the Ayvalık region in NW Turkey using a GIS-based heuristic approach.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis

2009

Self Cite

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This paper presents the results of geographical information system (GIS)-based landslide susceptibility mapping in Ayvalık, western Turkey using multi-criteria decision analysis. The methodology followed in the study includes data production, standardization, and analysis stages. A landslide inventory of the study area was compiled from aerial photographs, satellite image interpretations, and detailed field surveys. In total, 45 landslides were recorded and mapped. The areal extent of the landslides is 1.75 km 2 . The identified landslides are mostly shallowseated, and generally exhibit progressive character. They are mainly classified as rotational, planar, and toppling failures. In all, 51, 45, and 4% of the landslides mapped are rotational, planar, and toppling types, respectively. Morphological, geological, and land-use data were produced using existing topographical and relevant thematic maps in a GIS framework. The considered landslide-conditioning parameters were slope gradient, slope aspect, lithology, weathering state of the rocks, stream power index, topographical wetness index, distance from drainage, lineament density, and land-cover and vegetation density. These landslide parameters were standardized in a common data scale by fuzzy membership functions. Then, the degree to which each parameter contributed to landslides was determined using the analytical hierarchy process method, and the weight values of these parameters were calculated.The weight values obtained were assigned to the corresponding parameters, and then the weighted parameters were combined to produce a landslide susceptibility map. The results obtained from the susceptibility map were evaluated with the landslide location data to assess the reliability of the map. Based on the findings obtained in this study, it was found that 5.19% of the total area was prone to landsliding due to the existence of highly and completely weathered lithologic units and due to the adverse effects of topography and improper land use.

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Landslide susceptibility assessment in the İzmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method

Cited by 50 publications

References 52 publications

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland

Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis

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