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

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 a...

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Vydané v:Landslides Ročník 17; číslo 1; s. 217 - 229
Hlavní autori: Huang, Faming, Zhang, Jing, Zhou, Chuangbing, Wang, Yuhao, Huang, Jinsong, Zhu, Li
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2020
Springer Nature B.V
Predmet:
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
Deep learning
Back-propagation neural network
Landslide susceptibility prediction
Support vector machine
Fully connected sparse autoencoder
ISSN:1612-510X, 1612-5118
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1612-510X
1612-5118
DOI:10.1007/s10346-019-01274-9