Study on landslide deformation prediction based on recurrent neural network under the function of rainfall

Authors:
Huangqiong Chen;Zhigang Zeng;Huiming Tang
Affiliations:
Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China,Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China ...;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China,Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China ...;Faculty of Engineering, China University of Geosciences, Wuhan, Hubei, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Year:
2012

Citing 2
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Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

Computers & Geosciences

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Abstract

Landslide deformation prediction has significant practical value that can provide guidance for preventing the disaster and guarantee the safety of people's life and property. In this paper, a method based on recurrent neural network (RNN) for landslide prediction is presented. The results show that the prediction accuracy of RNN model is much higher than the feedforward neural network model for Baishuihehe landslide. Therefore, the RNN model is an effective and feasible method to further improve accuracy for landslide displacement prediction.