A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landsli
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A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide Yong‑gang Zhang1,2,3 · Jun Tang4,5 · Zheng‑ying He1 · Junkun Tan6 · Chao Li7 Received: 5 May 2020 / Accepted: 19 September 2020 © Springer Nature B.V. 2020
Abstract Landslides are natural phenomena, causing serious fatalities and negative impacts on socioeconomic. The Three Gorges Reservoir (TGR) area of China is characterized by more prone to landslides for the rainfall and variation of reservoir level. Prediction of landslide displacement is favorable for the establishment of early geohazard warning system. Conventional machine learning methods as forecasting models often suffer gradient disappearance and explosion, or training is slow. Hence, a dynamic method for displacement prediction of the step-wise landslide is provided, which is based on gated recurrent unit (GRU) model with time series analysis. The establishment process of this method is interpreted and applied to Erdaohe landslide induced by multi-factors in TGR area: the accumulative displacements of landslide are obtained by the global positioning system; the measured accumulative displacements is decomposed into the trend and periodic displacements by moving average method; the predictive trend displacement is fitted by a cubic polynomial; and the periodic displacement is obtained by the GRU model training. And the support vector machine (SVM) model and GRU model are used as comparisons. It is verified that the proposed method can quite accurately predict the displacement of the landslide, which benefits for effective early geological hazards warning system. Moreover, the proposed method has higher prediction accuracy than the SVM model. Keywords Step-wise landslide · Displacement prediction · Moving average method · Gated recurrent unit model · Global positioning system (GPS) technology * Zheng‑ying He [email protected] 1
Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China
2
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221000, China
3
China Geological Survey, Beijing 100037, China
4
Xiamen Xijiao Hard Science Industrial Technology Research Institute Co., Ltd, Xiamen 316000, China
5
College of Civil Engineering, Huaqiao University, Xiamen 316000, China
6
School of Civil Engineering, Central South University, Changsha 410083, China
7
School of Mechanical Engineering, Tianjin University, Tianjin 300072, China
13
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Natural Hazards
1 Introduction Landslides are universal phenomena, causing dangerous fatalities and negative impacts on socioeconomic (Flentje and Chowdhury 2018; Juliev et al. 2019; Kaur and Sood 2020; Wang et al. 2019a; Bao et al. 2019). From 2004 to 2010, a cumulative total of 2,620 fatal non-seismic landslides worldwide led to 32,322 deaths (Petley and David 2012). Schuster and Highland (2001) show that 4000–5000 deaths were recorded due to landslides in Mount Huascaran, Peru, in 1962, and over 18,000
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