Prediction of landslide displacement based on EEMD-Prophet-LSTM

WANG Zhenhao; NIE Wen; XU Hanhua; JIAN Wenbin

doi:10.7523/j.ucas.2022.002

Journal of University of Chinese Academy of Sciences >

2023 , Vol. 40 >Issue 4: 514 - 522

DOI: https://doi.org/10.7523/j.ucas.2022.002

Research Articles

Prediction of landslide displacement based on EEMD-Prophet-LSTM

WANG Zhenhao ,
NIE Wen ,
XU Hanhua ,
JIAN Wenbin

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1 Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 Kunming Prospecting Design Institute of China Nonferrous Metals Industry Co., Ltd, Kunming 650051, China;
4 Yunnan Key Laboratory of Geotechnical Engineering and Geohazards, Kunming 650051, China;
5 Institute of Geotechnical and Geological Engineering, Fuzhou University, Fuzhou 350116, China

Received date: 2021-10-19

Revised date: 2022-01-06

Online published: 2022-01-06

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Abstract

For the unsteady process of step-type landslide displacement, a method combining ensemble empirical mode decomposition (EEMD), Prophet, and long short time memory network (LSTM) to predict landslide displacement is proposed. The displacement data of Baishuihe landslide was taken as examples. The displacement time series was decomposed into residual(RES) and several intrinsic mode functions(IMF) by EEMD. The superimposition of IMFS which included periodic factors and random factors was considered as a volatility item, and the RES was regarded as a trend term. The trending term was fitted by the Prophet and the the volatility term was predicted by LSTM. The addition of the two prediction results was the predictied value of the landslide displacement. The results show that the coefficient of determination(R²) of the EEMD-Prophet-LSTM model is above 0.98 for Baishuihe landslide displacement prediction, which is better than traditional machine learning methods such as support vector machine and artificial neural network. Moreover, the prediction accuracy R² of this method for each monitoring point of the Bazimen landslide is also above 0.96, which proves the applicability of this method.

Key words： landslide displacement; time series; ensemble empirical mode decomposition; Prophet; long short time memory network

Cite this article

WANG Zhenhao , NIE Wen , XU Hanhua , JIAN Wenbin . Prediction of landslide displacement based on EEMD-Prophet-LSTM[J]. Journal of University of Chinese Academy of Sciences, 2023 , 40(4) : 514 -522 . DOI: 10.7523/j.ucas.2022.002

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