Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia

Abstract

Landslide susceptibility is an important topic mainly because its geo-spatial analysis provides a useful tool for planning, disaster management and hazard mitigation. In this study, the aim is to identify and analyze landslide susceptibility at a local spatial scale, which is represented by the town of Handlov a, using the multicriteria evaluation (i.e., the analytical hierarchy process technique -AHP) and geographic information systems (GIS). The following landslide conditioning factors were selected representing the local terrain predispositions: slope angle, geology, slope aspect, elevation, distance from rivers, distance from faults and land use. The raster-based analysis was performed using the spatial resolution of 10 Â 10 m. The weights for each factor were determined by the AHP technique where slope angle had the highest relative importance. Based on the resulting susceptibility map, 51.98% out of the total study area is characterized by high and very high susceptibility class. The Atlas of Slope Stability of the Slovak Republic, which contains past landslides until 2006, was used for verification of the results. The verification confirmed a moderate accuracy between the landslide susceptibility map and landslide inventory from the atlas since 60.8% of all landslide areas from the atlas corresponded with high and very high susceptibility class. ARTICLE HISTORY

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

“…Landslide susceptibility mapping (LSM) focuses on the identification of the vulnerable areas to potential landslides based on the predispositions which characterize the propensity for landsliding (Fell et al 2008;Vojtekov a and Vojtek 2020). For that reason, the LS modelling is based on different conditioning factors, which are represented by digital elevation model-derived factors, such as altitude, slope, aspect; meteorological factors, such as annual rainfall or solar radiation; river networkderived factors, such as stream density or distance to rivers; geological factors, such as geological/lithological units and distance to faults; soil factors, land use/land cover factors (Westen et al 2008).…”

A comparative evaluation of machine learning algorithms and an improved optimal model for landslide susceptibility: a case study

“…Direct problems are usually investigated via point-to-point monitoring, while indirect methods focus on natural factors to analyze the landslide formation mechanism (Xu et al 2014;Fang et al 2020;Yuan et al 2021). For example, streams play an important role in the formation of unstable slopes in mountainous areas (Vojtekov a and Vojtek 2020). Physical models are commonly employed in landslide analysis to account for streams (e.g., examining the role of vegetation via slope stability models or man-made factors) (Pham et al 2016;Wiesmair et al 2017).…”

Geohazards Affecting Cultural Heritage Monuments. A Complex Case Study From Romania

“…But one thing is certain, namely the need to protect and preserve these living evidences of the past. This requires a better understanding of the risk factors and the initiation of diagnostic studies in order to implement measures to mitigate the risks and their consequences (Vojtekova and Vojtek, 2020). The technological progress of mankind has created many alternative methods to the traditional ones of risk assessment and conservation of cultural heritage elements (Pavlidis et al 2007).…”