Displacement Prediction of Reservoir Bank Landslide Based on Optimal Decomposition Mode and GRU Model

LUO Huiyuan; JIANG Ya’nan; XU Qiang; TANG Bin

doi:10.13203/j.whugis20200610

Volume 48 Issue 5

May 2023

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Geomatics and Information Science of Wuhan University > 2023 > 48(5): 702-709. > DOI: 10.13203/j.whugis20200610

LUO Huiyuan, JIANG Ya’nan, XU Qiang, TANG Bin. Displacement Prediction of Reservoir Bank Landslide Based on Optimal Decomposition Mode and GRU Model[J]. Geomatics and Information Science of Wuhan University, 2023, 48(5): 702-709. DOI: 10.13203/j.whugis20200610

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Displacement Prediction of Reservoir Bank Landslide Based on Optimal Decomposition Mode and GRU Model

1.
State Key Laboratory of Geological Hazard Prevention and Geological Environment Protection, Chengdu University of Technology, Chengdu 610059, China
2.
School of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China

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Received Date: October 25, 2021
Available Online: May 23, 2023
Published Date: May 04, 2023

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Abstract

Abstract

Objectives The inadequate utilization of multisource monitoring data and the unstable results of displacement prediction are often caused by inaccurate extraction of random components, uncertain optimal training data set and timeliness in the comprehensive landslide displacement prediction study.
Methods On that account, a new landslide prediction model is proposed by integrating the variational mode decomposition with the gated recurrent unit recurrent neural network on the basis of landslide displacement time series analysis.
Results Taking Baishuihe landslide in the Three Gorges Reservoir Area as an example, the monitoring data including displacement and reservoir water level and rainfall data from July 2003 to December 2012 are selected for analysis and research. The root mean square error of the predicted value of the model is 9.715 mm and the coefficient of determination is 0.967. The results show that the model guarantees high prediction accuracy and has obvious advantages in effectiveness and timeliness as well.
Conclusion Therefore, it has a strong application and popularization value in reservoir bank landslide displacement prediction research.

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