A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods

被引:48
作者
Zhao, Pengxiang [1 ]
Masoumi, Zohreh [2 ,3 ]
Kalantari, Maryam [2 ]
Aflaki, Mahtab [2 ]
Mansourian, Ali [1 ,4 ]
机构
[1] Lund Univ, Dept Phys Geog & Ecosyst Sci, S-22362 Lund, Sweden
[2] Inst Adv Studies Basic Sci IASBS, Dept Earth Sci, Zanjan 4513766731, Iran
[3] Ctr Res Climate Change & Global Warming CRCC, Zanjan 4513766731, Iran
[4] Lund Univ, Ctr Middle Eastern Studies, S-22362 Lund, Sweden
关键词
landslide susceptibility mapping; machine learning; deep learning; landslide causative factors; feature importance; CONVOLUTIONAL NEURAL-NETWORKS; GROUNDWATER POTENTIAL ZONES; LOGISTIC-REGRESSION; ENSEMBLES; WEIGHT; COUNTY; AREA; MAP;
D O I
10.3390/rs14010211
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43-85.6%, AUC = 0.934-0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management.
引用
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页数:22
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