GIS-based landslide susceptibility mapping using frequency ratio and index of entropy models for She County of Anhui Province, China

Abstract

Landslides caused countless economic and casualty losses in China, especially in mountainous and hilly areas. Landslide susceptibility mapping is an important approach and tool for landslide disaster prevention and control. This study presents a landslide susceptibility assessment using frequency ratio (FR) and index of entropy (IOE) models within a geographical information system for She County in the mountainous region of South Anhui, China. First, the landslide locations were ascertained in the study area using historical landslide records, aerial photographs, and multiple field surveys. In all, 502 landslides were identified and randomly divided into two groups as training (70%) and validation (30%) datasets. Additionally, the landslide-influencing factors, including slope angle, slope aspect, curvature, landform, lithology, distance to faults, distance to roads, distance to rivers, rainfall, and normalized difference vegetation index, were selected and their relative importance and weights were determined by FR and IOE models. The results show that the very high and high susceptibility classes cover nearly 50% of the study area. Finally, the comprehensive performance of the two models was validated and compared using receiver operating characteristic curves. The results demonstrated that the IOE model with the area under the curve (AUC) of 0.802, which is slightly better in prediction than the FR model (AUC = 0.786). The interpretation of the susceptibility map indicated that landform, slope degree, and distance to rivers plays a major role in landslide occurrence and distribution. The research results can be used for preliminary land use planning and hazard mitigation purposes.

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

“…The IOE model is focused on the assumption of bivariate evaluation, where the number of landslides inside a specific variable is calculated [ 106 , 107 , 108 ]. This method enables the weights for every input factor to be calculated [ 109 , 110 , 111 ]. The following procedures are employed to determine the weight value for the variable in its entirety or the information coefficient [ 112 ]: …”

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

“…The fieldwork carried out showed the diversity of landslide patterns, including rainfall-induced rapid mass movements, rock falling, bouncing and rolling, shallow landslides and debris and mud flow. These patterns represent a direct reflection of the complex interaction between natural and human characteristics in the study area, Bivariate statistical methods are flexible in implementation with spatial outcomes that assist in managing the landslide risk (Liu et al 2022;Kincal and Kayhan 2022). In this investigation, the FR model provided the best performance in comparison with the SI and IoE models.…”