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Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks

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Abstract

Displacement prediction plays a significant role in the landslide disaster early warning. However, landslide deformation is a complex nonlinear dynamic process, posing difficulties in the displacement prediction especially for the commonly used static models. This study applies an advanced deep machine learning method called gated recurrent unit (GRU) to the displacement prediction of the Jiuxianping landslide, which is a typical reservoir landslide located in the Yunyang County of Chongqing, China. Results show that the GRU-based approach is able to portray the variation of the periodic displacement in the testing dataset with fewer outliers. Although both the artificial neural network (ANN) and random forest regression (RFR) can capture the variation tendency of data points in the training dataset, they are unable to predict the local peaks well in the testing dataset. For the multivariate adaptive regression splines (MARS), the deformation characteristics of the periodic displacement curve cannot be well captured, and the overall predictive performance is unsatisfactory. Different from the three static models, the GRU model is essentially a dynamic model making full use of the historical information, which can portray the deformation characteristics of the Jiuxianping landslide rationally.

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Acknowledgements

The authors are grateful to the financial supports from National Key R&D Program of China (2019YFC1509605), National Natural Science Foundation of China (52008058), Program of Distinguished Young Scholars, Natural Science Foundation of Chongqing, China (cstc2020jcyj-jq0087), and China Postdoctoral Science Foundation (2021M700608). The original data and codes used in this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. School of Civil Engineering, Chongqing University, Chongqing, 400045, China

    Wengang Zhang, Hongrui Li, Libin Tang, Xin Gu, Luqi Wang & Lin Wang

  2. Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Chongqing, 400045, China

    Wengang Zhang, Luqi Wang & Lin Wang

  3. National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, Chongqing, 400045, China

    Wengang Zhang, Luqi Wang & Lin Wang

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  1. Wengang Zhang
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Correspondence to Lin Wang.

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Zhang, W., Li, H., Tang, L. et al. Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks. Acta Geotech. 17, 1367–1382 (2022). https://doi.org/10.1007/s11440-022-01495-8

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