- Title
- A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
- Creator
- Huang, Faming; Zhang, Jing; Zhou, Chuangbing; Wang, Yuhao; Huang, Jinsong; Zhu, Li
- Relation
- Landslides Vol. 17, Issue 1, p. 217-229
- Publisher Link
- http://dx.doi.org/10.1007/s10346-019-01274-9
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2020
- Description
- 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.
- Subject
- landslide susceptibility prediction; deep learning; fully connected sparse autoencoder; support vector machine; back-propagation neural network
- Identifier
- http://hdl.handle.net/1959.13/1424739
- Identifier
- uon:38139
- Identifier
- ISSN:1612-510X
- Language
- eng
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