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Multi Step Prediction of Landslide Displacement Time Series Based on Extended Kalman Filter and Back Propagation Trough Time

  • Ping Jiang
  • Jiejie Chen
  • Zhigang ZengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)

Abstract

Landslide is a complex geological natural disaster that brings harm or damage to human beings and their living environment. By strengthening landslide monitoring and forecasting technology, people can avoid or reduce the impact of disasters more reasonably. At present, the single step prediction of landslide displacement time series mainly uses t time to predict the data of t+1 moment, which obviously makes it difficult for people to take appropriate measures to deal with landslide changes. In this paper, a time reverse recursive algorithm based on extended Kalman filter (EKF)and Back propagation trough time (BPTT) method, is used to predict landslide displacement in order to extend the time width of landslide prediction. The EKF is firstly used to optimize the BPTT weights, and then the network parameters are adjusted in real time to improve the reliability of the prediction. Finally, the landslide displacement data of Liangshuijing (LSJ) in the three Gorges Reservoir area is used as experimental samples to verify the feasibility and practicability of EKF-BPTT.

Keywords

Landslide Time series Prediction EKF-BPTT 

Notes

Acknowledgements

The work was supported by the Natural Science Foundation of China under Grants 61841301, 61603129 and 61673188, the Research Project of Hubei Provincial Department of Education under Grant Q20184504, the Scientific Research Project of Hubei PolyTechnic University under Grant 18xjz02C.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer SchoolHubei Polytechnic UniversityHuangshiChina
  2. 2.College of Computer Science and TechnologyHubei Normal UniversityHuangshiChina
  3. 3.School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina

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