Journal Article10.1007/S10346-019-01274-9
A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
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TL;DR: The asymmetric and unsupervised FC-SAE can extract optimal non-linear features from environmental factors successfully, outperforms some conventional machine learning methods, and is promising for LSP.
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Abstract: The environmental factors of landslide susceptibility are generally uncorrelated or non-linearly correlated, resulting in the limited prediction performances of conventional machine learning methods for landslide susceptibility prediction (LSP). Deep learning methods can exploit low-level features and high-level representations of information from environmental factors. In this paper, a novel deep learning–based algorithm, the fully connected spare autoencoder (FC-SAE), is proposed for LSP. The FC-SAE consists of four steps: raw feature dropout in input layers, a sparse feature encoder in hidden layers, sparse feature extraction in output layers, and classification and prediction. The Sinan County of Guizhou Province in China, with a total of 23,195 landslide grid cells (306 recorded landslides) and 23,195 randomly selected non-landslide grid cells, was used as study case. The frequency ratio values of 27 environmental factors were taken as the input variables of FC-SAE. All 46,390 landslide and non-landslide grid cells were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide/non-landslide data, the performances of the FC-SAE and two other conventional machine learning methods, support vector machine (SVM) and back-propagation neural network (BPNN), were compared. The results show that the prediction rate and total accuracies of the FC-SAE are 0.854 and 85.2% which are higher than those of the SVM-only (0.827 and 81.56%) and BPNN (0.819 and 80.86%), respectively. In conclusion, the asymmetric and unsupervised FC-SAE can extract optimal non-linear features from environmental factors successfully, outperforms some conventional machine learning methods, and is promising for LSP.
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Citations
Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
TL;DR: It can be inferred that the machine learning models have higher LSP performance than general statistical and heuristic models due to its high AUC accuracy and reasonable LSIs distribution features, while general statistical model is limited by its linear analysis and heuristics limited by subjective weighting process.
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A spatially explicit deep learning neural network model for the prediction of landslide susceptibility
Dong Van Dao,Abolfazl Jaafari,Mahmoud Bayat,Davood Mafi-Gholami,Chongchong Qi,Hossein Moayedi,Tran Van Phong,Hai-Bang Ly,Tien-Thinh Le,Phan Trong Trinh,Chinh Luu,Nguyen Kim Quoc,Bui Nhi Thanh,Binh Thai Pham +13 more
TL;DR: A comparative analysis using the Wilcoxon signed-rank tests revealed a significant improvement of landslide prediction using the spatially explicit DL model over the quadratic discriminant analysis, Fisher's linear discriminantAnalysis, and multi-layer perceptron neural network.
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Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models
TL;DR: It can be concluded that selecting recorded landslides as prior knowledge to train and test the LSP models is the key reason for the higher prediction accuracy of the SML models, while the lack of a priori knowledge and target guidance is an important reasons for the low LSP accuracy ofThe USML models.
Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model
TL;DR: The SSMLP model successfully addresses the drawbacks existed in the conventional machine learning for LSP and has a considerably higher LSP performance than the MLP and K-means clustering in Xunwu County.
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A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping
TL;DR: Four heterogeneous ensemble-learning techniques, that is, stacking, blending, simple averaging, and weighted averaging, are introduced to predict landslide susceptibility in Yanshan County, China to show higher prediction accuracy than the individual classifiers mentioned above based on statistical measures.
218
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