Physically Based Susceptibility Assessment of Rainfall-Induced Shallow Landslides Using a Fuzzy Point Estimate Method

Abstract

Abstract:The physically based model has been widely used in rainfall-induced shallow landslide susceptibility analysis because of its capacity to reproduce the physical processes governing landslide occurrence and a higher predictive capability. However, one of the difficulties in applying the physically based model is that uncertainties arising from spatial variability, measurement errors, and incomplete information apply to the input parameters and analysis procedure. Uncertainties have been recognized as an important cause of mismatch between predicted and observed distributions of landslide occurrence. Therefore, probabilistic analysis has been used to quantify the uncertainties. However, some uncertainties, because of incomplete information, cannot be managed satisfactorily using a probabilistic approach. Fuzzy set theory is applicable in this case. In this study, in order to handle uncertainty propagation through a physical model, fuzzy set theory, coupled with the vertex method and the point estimate method, was adopted for regional landslide susceptibility assessment. The proposed approach was used to evaluate susceptibility to rainfall-induced shallow landslides for a regional study area, and the analysis results were compared with landslide inventory to evaluate the performance of the proposed approach. The AUC values arising from the landslide susceptibility analyses using the proposed approach and probabilistic analysis were 0.734 and 0.736, respectively. However, when the COV values of the input parameters were reduced, the AUC values of the proposed approach and the probabilistic analysis were reduced to 0.722 and 0.688, respectively. It means that the performance of the fuzzy approach is similar to that of probabilistic analysis but is more robust against variation of input parameters. Thus, at catchment scale, the fuzzy approach can respond appropriately to the uncertainties inherent in physically based landslide susceptibility analysis, and is especially advantageous when the amount of quality data is very limited.

Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models

“…To obtain reasonable LSP results, it is crucial to select appropriate prediction models that accept the landslide-relevant thematic information. Conventionally, the LSP models can be divided into these types as probability analysis models [5], heuristic models [1], deterministic models [6] and statistical models [7]. On the whole, these types of models contribute to the development of LSP and are regarded as effective technologies.…”

Remote Sensing of Landslides—A Review

“…Landslides are mainly triggered by natural factors, such as the 2008 Wenchuan earthquake, China [16], freeze/thaw in the Qinghai-Tibet engineering corridor [15], and heavy rainfalls in different regions reported in this special issue [2,3,8,9,17,19]. On the other hand, landslides can also be caused by anthropogenic activities, including rapid urbanization through hill-cutting in densely populated Bangladesh [17], impoundment of the reservoir in the Three Gorges Reservoir of the Yangtze River, China [11], under-construction of the reservoir in the eastern Qinghai-Tibet plateau in the southwestern part of China [3], and underground mining in the northern Shaanxi province, China [5].…”

“…Bivariate and multivariate statistical techniques, through the use of Dempster-Shafer weights of evidence and multiple regression methods [17], and techniques that combine principle component analysis and fuzzy membership techniques were used and tested [18]. A physically based model was applied in the rainfall-induced shallow landslide susceptibility analysis because of its ability to reproduce the physical processes governing landslide occurrence [19]. In addition, landslide activity was assessed by the means of an activity matrix derived from InSAR measurements from descending and ascending passes [1].…”

“…Landslide susceptibility mapping was presented by integrating information theory, K-means cluster analysis and statistical models in the Three Gorges Area, China [18]. The regional landslide susceptibility and uncertainty assessment in Gangwon Province, Republic of Korea was obtained by fuzzy set theory coupled with the vertex and the point estimate methods [19].…”

Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China

“…2017, 6, 347 2 of 16 factors and landslides, not only avoid the dependence of mechanical models on high-precision data, but also reduce the subjectivity brought by expert evaluation statistical methods [8,9]. The statistical methods include the weighted liner combination model (WLC) [10,11], the logistic regression model [12][13][14], fuzzy synthetic evaluation model [15,16], and neural network model [17,18].…”