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Landslides

, Volume 17, Issue 1, pp 217–229 | Cite as

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

  • Faming Huang
  • Jing Zhang
  • Chuangbing Zhou
  • Yuhao Wang
  • Jinsong Huang
  • Li ZhuEmail author
Technical Note

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.

Keywords

Landslide susceptibility prediction Deep learning Fully connected sparse autoencoder Support vector machine Back-propagation neural network 

Abbreviations

LSP

Landslide susceptibility prediction

FC-SAE

Fully connected spare autoencoder

SVM

Support vector machine

BPNN

Back-propagation neural network

LSI

Landslide susceptibility index

BSI

Bare land soil index

LSM

Landslide susceptibility map

NPR

Negative predictive ratio

ANN

Artificial neural network

FR

Frequency ratio

ReLU

Rectified linear unit

DEM

Digital elevation model

MNDWI

Modified normalized difference water index

NDVI

Normalized difference vegetation index

PPR

Positive predictive ratio

TR

Total accuracy

Notes

Acknowledgements

We thank the Sinan Land Resources Bureau, Guizhou Province of China, for providing the landslide inventory information.

Funding information

This research is funded by the Natural Science Foundation of China (Nos. 41807285 and 51679117), the Science and Technology Department of Jiangxi Province Outstanding Youth Fund Project (No. 2018ACB21038), the National Science Foundation of Jiangxi Province, China (No. 20192BAB216034), and the China Postdoctoral Science Foundation (No. 2019M652287).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Civil Engineering and ArchitectureNanchang UniversityNanchangChina
  2. 2.School of Information EngineeringNanchang UniversityNanchangChina
  3. 3.ARC Centre of Excellence for Geotechnical Science and EngineeringUniversity of NewcastleNewcastleAustralia

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