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Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks
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.
Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks
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.
Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks
Acta Geotech.
Zhang, Wengang (Autor:in) / Li, Hongrui (Autor:in) / Tang, Libin (Autor:in) / Gu, Xin (Autor:in) / Wang, Luqi (Autor:in) / Wang, Lin (Autor:in)
Acta Geotechnica ; 17 ; 1367-1382
01.04.2022
16 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Machine learning , Displacement prediction , Jiuxianping landslide , Gated recurrent unit , Time series Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
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