Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China

Abstract

The aim of this study is to provide a landslide susceptibility map of the Xunyang District of a mountainous terrain, at the southern part of the Qin-Ba Mountain Region, which has been highly exposed to widely distributed shallow landslides over the past few decades. The Weight of Evidence (WoE) method was adopted in this research considering both the presence of a certain landslide causative factor class and the absence of remaining classes, which was used for determining a clearly spatial correlation between a landslide occurrence and the causative factors. Intrinsic factors, including geomorphological factors, geological factors, and river flow networks, and external factors of anthropogenic engineering activities in terms of density of road network were all considered and involved in the Geological Information System (GIS) environment for reconstructing the thematic layers of factor dataset. Significant assumptions prior to the analysis were emphasized to ensure conditional independence between each pair of factors for this bivariate statistical approach. In addition, a detailed landslide inventory map was constructed through field investigation and a remote sensing interpretation process at a scale of 1:50000. The thematic layers and landslide map were overlapped to obtain a spatial statistical relationship by using the frequency ratio method. At last, the validation process for the derived susceptibility map was conducted by applying the ROC curve, indicating that more than 90% of the landslides were in categories of high and moderate susceptibility zones. The causative factor classes, including the slope angles ranging from 20 to 40°, strong weathered and fractured strata, and road network density were identified to considerably influence the landslide distribution in the study area. The results have proven to be significantly meaningful for landslide hazard risk mitigation and land use management for the local authorities responsible for these fields.

A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

“…When the surface saturation increases, the rock and soil strength will decrease, thereby increasing the risk of the landslides [ 29 ]. The topographic humidity index (TWI) spatial distribution map of the study area is obtained based on the ArcGIS platform [ 30 ]. The TWI value of the study area is between −0.06 and 20.5, and it is divided into six categories by natural breakpoint method (as shown in Figure 3(a) – 3(l) ).…”

Landslide susceptibility mapping (LSM) of Swat District, Hindu Kush Himalayan region of Pakistan, using GIS-based bivariate modeling

“…Researchers have used this method for mineral exploration mapping. Later on, this method was widely used in landslide prediction mapping due to its authentic results compared to field and other deterministic approaches (Cao et al, 2021).…”

GIS-Remote Sensing Based Approach in Assessing and Modeling Landslide Vulnerability Areas in Southeast Zone of Nigeria

“…Some researchers had identi ed some of the factors that can initiate or trigger landslides within the area as rainfall, gully erosion, anthropogenic activities, geology, elevation, slope angle and soil texture and type (Okagbue 1989;1992;Igwe, 2015). Others are land use land cover change (LULCC), aspect, curvature, drainage density, lineament density, normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), topographic wetness index (TWI), geomorphology, temperature, wind speed, wind pressure, streams head cut, settlements, and roads network construction (Cao et al, 2021;E ong et al, 2021;Woldearegay, 2013;Wubalem and Meten, 2020;Shahabi and Hashim, 2015;Ayadiuno et al, 2022). These factors whose combined present or absent are capable of triggering landslide have caused avoidable severe destructions and unquanti able losses in the study area.…”