Paper Info
Title
A Comparative Analysis of Certainty Factor-Based Machine Learning Methods for Collapse and Landslide Susceptibility Mapping in Wenchuan County, China
Abstract
After the "5 center dot 12" Wenchuan earthquake in 2008, collapses and landslides have occurred continuously, resulting in the accumulation of a large quantity of loose sediment on slopes or in gullies, providing rich material source reserves for the occurrence of debris flow and flash flood disasters. Therefore, it is of great significance to build a collapse and landslide susceptibility evaluation model in Wenchuan County for local disaster prevention and mitigation. Taking Wenchuan County as the research object and according to the data of 1081 historical collapse and landslide disaster points, as well as the natural environment, this paper first selects six categories of environmental factors (13 environmental factors in total) including topography (slope, aspect, curvature, terrain relief, TWI), geological structure (lithology, soil type, distance to fault), meteorology and hydrology (rainfall, distance to river), seismic impact (PGA), ecological impact (NDVI), and impact of human activity (land use). It then builds three single models (LR, SVM, RF) and three CF-based hybrid models (CF-LR, CF-SVM, CF-RF), and makes a comparative analysis of the accuracy and reliability of the models, thereby obtaining the optimal model in the research area. Finally, this study discusses the contribution of environmental factors to the collapse and the landslide susceptibility prediction of the optimal model. The research results show that (1) the areas prone to extremely high collapse and landslide predicted by the six models (LR, CF-LR, SVM, CF-SVM, RF and CF-RF) have an area of 730.595 km(2), 377.521 km(2), 361.772 km(2), 372.979 km(2), 318.631 km(2), and 306.51 km(2), respectively, and the frequency ratio precision of collapses and landslides is 0.916, 0.938, 0.955, 0.956, 0.972, and 0.984, respectively; (2) the ranking of the comprehensive index based on the confusion matrix is CF-RF>RF>CF-SVM>CF-LR>SVM>LR and the ranking of the AUC value is CF-RF>RF>CF-SVM>CF-LR>SVM>LR. To a certain extent, the coupling models can improve precision more over the single models. The CF-RF model ranks the highest in all indexes, with a POA value of 257.046 and an AUC value of 0.946; (3) rainfall, soil type, and distance to river are the three most important environmental factors, accounting for 24.216%, 22.309%, and 11.41%, respectively. Therefore, it is necessary to strengthen the monitoring of mountains and rock masses close to rivers in case of rainstorms in Wenchuan county and other similar areas prone to post-earthquake landslides.
Year
DOI
Venue
2022
10.3390/rs14143259
REMOTE SENSING
Keywords
DocType
Volume
collapse and landslide disaster, susceptibility evaluation, machine learning model, certainty factor, Wenchuan county
Journal
14
Issue
ISSN
Citations 
14
2072-4292
0
PageRank 
References 
Authors
0.34
0
13
Name
Order
Citations
PageRank
Xinyue Yuan100.68
Chao Liu212.72
Ruihua Nie301.69
Zhengli Yang402.03
Weile Li504.06
Xiaoai Dai601.69
Junying Cheng700.34
Junmin Zhang800.68
Lei Ma9335.90
Xiao Fu1011.03
Min Tang1111.03
Yina Xu1200.68
Heng Lu1302.03