A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction

• Published in: Landslides 2020-01, Vol.17 (1), p.217-229
• Main Authors: Huang, Faming, Zhang, Jing, Zhou, Chuangbing, Wang, Yuhao, Huang, Jinsong, Zhu, Li
• Format: Article
• Language: English
• Subjects: Agriculture; Algorithms; Artificial neural networks; Cells; Civil Engineering; Coders; Datasets; Deep learning; Earth and Environmental Science; Earth Sciences; Environmental factors; Feature extraction; Geography; Landslides; Learning algorithms; Machine learning; Natural Hazards; Neural networks; Support vector machines; Technical Note; Training
• ISSN: 1612-510X; 1612-5118
• DOI: 10.1007/s10346-019-01274-9

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.