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Landslide Displacement Prediction Based on Multivariate LSTM Model

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  • Gonghao Duan

    (School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China
    Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
    Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China)

  • Yangwei Su

    (School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China
    Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan 430205, China)

  • Jie Fu

    (Center For Hydrogeology and Environmental Geology Survey, China Geological Survey, Baoding 071051, China)

Abstract

There are many frequent landslide areas in China, which badly affect local people. Since the 1980s, there have been more than 200 landslides in China with a death toll of 30 or more people at a time, economic losses of more than CNY 10 million or significant social impact. Therefore, the study of landslide displacement prediction is very important. The traditional ARIMA and LSTM models are commonly used for forecasting time series data. In our study, a multivariable LSTM landslide displacement prediction model is proposed based on the traditional LSTM model, which integrates rainfall and reservoir water level data. Taking the Baijiabao landslide in the Three Gorges Reservoir area as an example, the data of displacement, rainfall and reservoir water level of monitoring point ZG323 from November 2006 to December 2012 were selected for this study. Our results show that the displacement prediction results of the multivariable LSTM model are more accurate than those of the ARIMA and the univariate LSTM models, and the mean square, root mean square and mean absolute errors are the smallest, which are 0.64223, 0.8014 and 0.50453 mm, respectively. Therefore, the multivariable LSTM model method has higher accuracy and better application prospects in the displacement prediction of the Baijiabao landslide, which can provide a certain reference for the displacement prediction of the same type of landslide.

Suggested Citation

  • Gonghao Duan & Yangwei Su & Jie Fu, 2023. "Landslide Displacement Prediction Based on Multivariate LSTM Model," IJERPH, MDPI, vol. 20(2), pages 1-16, January.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1167-:d:1029718
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    References listed on IDEAS

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    1. Rui Zhang & Zhen Guo & Yujie Meng & Songwang Wang & Shaoqiong Li & Ran Niu & Yu Wang & Qing Guo & Yonghong Li, 2021. "Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China," IJERPH, MDPI, vol. 18(11), pages 1-14, June.
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