Comparison of Effects between Different Weight Calculation Methods for Improving Regional Landslide Susceptibility—A Case Study from Xingshan County of China

Abstract

The information value (IV) model is a conventional method for landslide susceptibility prediction (LSP). However, it is inconsistent with the actual situation to regard all conditioning factors as equally weighted in the modeling process. In view of this, this paper studied the optimization effect of different weight calculation methods for IV model. Xingshan County, a typical landslide-prone area located in Hubei Province, China, was taken as a case study. The procedure was as follows: First, six conditioning factors, including elevation, slope angle, aspect, curvature, distance to river, and distance to road, were selected to form an evaluation factor library for analyzing the landslide susceptibility. Then, the weight of factors was calculated by fuzzy analytical hierarchy process (FAHP) and principal component analysis (PCA). On this basis, combined with the IV model, two weighted IV models (FAHP-IV model and PCA-IV model) were formed for LSP. The results shows that the optimization effect of PCA was the best. Moreover, compared with the IV-only model (AUC = 0.71), the FAHP-IV model (AUC = 0.76) and PCA-IV model (AUC = 0.79) performed better. The outcome also provided a feasible way for the study of regional LSP.

Improving the Accuracy of Regional Engineering Disturbance Disaster Susceptibility by Optimizing Weight Calculation Methods—A Case Study in the Himalayan Area, China

“…In terms of spatial distribution, the highway slope engineering disturbance disasters on the southern slope of the Himalayas are more developed, while the density of engineering disturbance disasters on the northern slope of the Himalayas is relatively small. Due to the large east-west span and large north-south elevation change in the study area, resulting in large differences in engineering geological conditions and complex distribution characteristics of engineering disturbance disasters, mathematical statistical analysis is a reasonable method to study the development law of engineering disturbance disasters [13].…”

“…In the comprehensive evaluation function, the weight of the principal component represents the degree of contribution, which reflects the proportion of the information contained in the original data in the principal component to the total information. The determination of weight by this method overcomes the defect of unreasonable determination of weight in some evaluation methods, and has the characteristics of rationality and objectivity [13]. In this study, PCA was conducted to derive the components that explained most of the variation of the 280 engineering disturbance disasters, and to extract some obvious features of disasters.…”

“…Disaster events are usually binary variables, that is, 0 and 1 represent the occurrence and non-occurrence of disaster events, respectively [13]. The LR model can reveal the multiple regression relationship between a dependent variable and multiple uncorrelated independent variables.…”

“…Therefore, to reduce the negative impact of engineering disturbance disasters, it is necessary to implement regional susceptibility evaluation [10,11]. According to the prediction principle, the susceptibility evaluation methods can be divided into four categories: physical model method, heuristic model, statistical model, and machine learning model [12,13]. The physical model requires detailed geotechnical parameters and is mainly aimed at the stability analysis of a single slope.…”

“…Because the occurrence of regional engineering disturbance disasters is affected by both control factors and impact factors, the contributions of the two to slope catastrophe are different, and the control factors of geological disasters in different regions are also different; those such as rainfall, lithology, and slope should have greater weight in the evaluation. Domestic and foreign scholars have conducted preliminary research in this area: for example, S. Lee et al [29] and N. N. Vasu et al [30] took the Yongin area and Mt Woomyeon area of Korea as the research area, respectively, and the susceptibility evaluation results obtained by using all index factors and only retaining the index factors with larger weights were compared; E. Yesilnacar and T. Topal [31] used statistical analysis and machine learning models to calculate the weights of index factors in the same region, and compared the results of the two methods; Cao et al [13] obtained the weight of each index through principal component analysis (PCA) and fuzzy analytic hierarchy process on the basis of the information value model, and optimized the information value model, which improved the accuracy of susceptibility evaluation by 5~8%. However, the research on the optimization of the engineering disturbance disaster susceptibility model is not enough, and at the same time, it is necessary to conduct in-depth research on the calculation method of the weight coefficient of the influencing factors [12].…”