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Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China

Author

Listed:
  • Qing Ling

    (Lanzhou University of Technology
    Chang’ an University)

  • Qin Zhang

    (Chang’ an University)

  • Jing Zhang

    (Chang’ an University)

  • Lingjie Kong

    (Lanzhou University of Technology)

  • Weiqi Zhang

    (The First Geodetic Team of the Ministry of Natural Resources)

  • Li Zhu

    (Information Engineering University)

Abstract

Prediction of landslide movement is an efficient approach in the reduction in landslide risk. However, it is also a tough task due to the scientific challenges in forecasting a sophisticated natural disaster. This paper proposes a VMD-MIC-M-KELM (variational mode decomposition-maximum information coefficient-multi-kernel extreme learning machine) technique for prediction of landslide movements. The original displacement is first decomposed into a predefined number of components by VMD. Then, the triggers of each component are selected based on MIC between subseries and influencing factors. The decomposed terms are predicted by M-KELM respectively via k-fold cross-validation. Finally, predicted total displacement is achieved by summing up all forecasting subseries. A case study of Miaodian landslide (China) is presented for validation of the developed model. The verification results demonstrate the higher ability of the approach to forecast monthly displacement for periods up to 12 months as compared to the Poly-KELM and SVR models. Thus, improved monthly predictions may be achieved with constantly updated datasets from the monitoring system, which would offer reliable information for early warning of landslide.

Suggested Citation

  • Qing Ling & Qin Zhang & Jing Zhang & Lingjie Kong & Weiqi Zhang & Li Zhu, 2021. "Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 925-946, August.
    • Handle: RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04713-w
    DOI: 10.1007/s11069-021-04713-w
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    Cited by:

    1. Shidong Wu & Hengrui Ma & Abdullah M. Alharbi & Bo Wang & Li Xiong & Suxun Zhu & Lidong Qin & Gangfei Wang, 2023. "Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting," Sustainability, MDPI, vol. 15(20), pages 1-23, October.
    2. Chenhui Wang & Wei Guo, 2023. "Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    3. Jiaqi Zhang & Xijun He, 2023. "Earthquake magnitude prediction using a VMD-BP neural network model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 189-205, May.

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