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Landslide Deformation Prediction Based on Recurrent Neural Network

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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. Genetic algorithm is used to optimize the initial weights and biases of the network. 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.

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Acknowledgments

The work is supported by the Natural Science Foundation of China under Grant 60974021, the 973 Program of China under Grant 2011CB710606, the Fund for Distinguished Young Scholars of Hubei Province under Grant 2010CDA081, and the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant 20100142110021.

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Authors and Affiliations

  1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan, 430074, China

    Huangqiong Chen & Zhigang Zeng

  2. Faculty of Engineering, China University of Geosciences, Wuhan, 430074, Hubei, China

    Huiming Tang

Authors
  1. Huangqiong Chen
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  2. Zhigang Zeng
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  3. Huiming Tang
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Correspondence to Huangqiong Chen.

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Chen, H., Zeng, Z. & Tang, H. Landslide Deformation Prediction Based on Recurrent Neural Network. Neural Process Lett 41, 169–178 (2015). https://doi.org/10.1007/s11063-013-9318-5

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