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Title: Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China
Author: Xing, Yin
Yue, Jianping
Guo, Zizheng
Chen, Yang
Hu, Jia Hu
Travé i Herrero, Anna
Keywords: Esllavissades
Aprenentatge automàtic
Machine learning
Issue Date: 17-Aug-2021
Publisher: Frontiers Media
Abstract: Integration of different models may improve the performance of landslide susceptibility assessment, but few studies have tested it. The present study aims at exploring the way to integrating different models and comparing the results among integrated and individual models. Our objective is to answer this question: Will the integrated model have higher accuracy compared with individual model? The Lvliang mountains area, a landslide-prone area in China, was taken as the study area, and ten factors were considered in the influencing factors system. Three basic machine learning models (the back propagation (BP), support vector machine (SVM), and random forest (RF) models) were integrated by an objective function where the weight coefficients among different models were computed by the gray wolf optimization (GWO) algorithm. 80 and 20% of the landslide data were randomly selected as the training and testing samples, respectively, and different landslide susceptibility maps were generated based on the GIS platform. The results illustrated that the accuracy expressed by the area under the receiver operating characteristic curve (AUC) of the BP-SVM-RF integrated model was the highest (0.7898), which was better than that of the BP (0.6929), SVM (0.6582), RF (0.7258), BP-SVM (0.7360), BP-RF (0.7569), and SVM-RF models (0.7298). The experimental results authenticated the effectiveness of the BP-SVM-RF method, which can be a reliable model for the regional landslide susceptibility assessment of the study area. Moreover, the proposed procedure can be a good option to integrate different models to seek an "optimal" result. Keywords: landslide susceptibility, random forest, integrated model, causal factor, GIS
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It is part of: Frontiers in Earth Science, 2021, vol. 9, p. 1-15
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ISSN: 2296-6463
Appears in Collections:Articles publicats en revistes (Mineralogia, Petrologia i Geologia Aplicada)

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