Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 19, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2021.3054029
Keywords
Feature extraction; Terrain factors; Environmental factors; Data models; Support vector machines; Predictive models; Mathematical model; Geographic information system (GIS); landslide susceptibility prediction (LSP); neural network; remote sensing (RS); sparse feature extraction (SFE)
Categories
Funding
- National Natural Science Foundation of China [41807285]
- Natural Science Foundation for Outstanding Young Scholars of Jiangxi Province [2018ACB21038]
- Natural Science Foundation of Jiangxi Province of China [20192BAB216034]
- Postdoctoral Science Foundation of China [2019M652287]
- Jiangxi Provincial Postdoctoral Science Foundation [2019KY08]
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This letter proposes a sparse feature extraction network (SFE+) for landslide susceptibility prediction (LSP), which improves the accuracy of traditional machine learning models in capturing nonlinear correlations among environmental factors. The SFE-based ML models show promising prospects for LSP.
Landslide susceptibility prediction (LSP) is a useful technology for landslide prevention. Due to the complex nonlinear correlations among environmental factors, traditional machine learning (ML) models have unsatisfactory LSP accuracies. In this letter, a sparse feature extraction network (SFE+) is proposed for LSP. First, the landslides and environmental factors are collected, and frequency ratios of environmental factors are calculated as the model inputs. Second, the input data are passed through the input layer with the dropout, and then, the features are passed through the hidden layers, that is, the k% lifetime sparsity layers. The hidden layers are employed to further sparse these factors to obtain the independent and redundant prediction features as much as possible. Finally, certain classifiers are used to realize the LSP in the study area. SFE-support vector machine (SVM), SFE-logistic regression (LR), and SFE-stochastic gradient descent (SGD) models are built. For comparison, principal component analysis (PCA)-SVM, PCA-LR, PCA-SGD, SVM, LR, and SGD models are also built for LSP in Shicheng County, China. Results show that the SFE-based ML models, especially the SFE-SVM, can effectively extract the sparse nonlinear features of environmental factors to improve LSP accuracies and have promising prospects for LSP.
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