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

Abstract

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.

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

“…On the other hand, Komac (2012) applied other bivariate Monte Carlo approach in Slovenia achieving also 70% of correctness. Similar results can be found in work (Mashari et al 2012;Akgun and Türk 2010;Kanungo et al 2006). According to the results, the presented approach provides diverse results for diverse study areas in the world.…”

Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method

“…Considering the fact that GIS-based landslide related criteria measured not only in different units but also in different scales of measurement (data types), such as nominal, ordinal, interval, and ratio scales [71], there is an urgent need for data standardisation. This rises from the inherent need to integrate all landslide criteria into the single output in the evaluation process.…”

“…The use of fuzzy sets within GIS-based hazard and susceptibility assessment has been demonstrated to have a good effect [71][72][73]. For this reason, fuzzy sets were used in this study.…”

“…In this study, three different membership functions have been employed for landslide susceptibility purpose including sigmoidal (s-shaped) FMFs, i.e., monotonically increasing and monotonically decreasing, user-defined fuzzy membership functions along with crisp membership functions are specified for each criterion (see Figure 4). The sigmoidal membership function is likely the most commonly used FMF in fuzzy set theory, and provides a gradual variation from non-membership (zero) to complete membership (one) [6,71,72,76], whereas it is sometimes inevitable to use user-defined FMFs or crisp membership functions. Nevertheless, all applied functions of criteria and the resultant output raster files are shown in Figures 4 and 5 …”

Landslides Hazard Assessment Using Different Approaches

“…Therefore, in order to achieve a landslide hazard, it is necessary to specify a time interval for potential events and also the magnitudes/intensities associated. Without an estimate of the likely timing of landslides occurrence, expressed as an annual frequency of events and without a certain level of magnitude, the users cannot make a quantitative estimate of hazard and the assessments remain only as a susceptibility evaluation (spatial probability) [8].…”