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Toward the reliable prediction of reservoir landslide displacement using earthworm optimization algorithm-optimized support vector regression (EOA-SVR)

Author

Listed:
  • Zhiyang Liu

    (China University of Geosciences
    China University of Geosciences)

  • Junwei Ma

    (China University of Geosciences
    China University of Geosciences)

  • Ding Xia

    (China University of Geosciences)

  • Sheng Jiang

    (China University of Geosciences
    China University of Geosciences)

  • Zhiyuan Ren

    (China University of Geosciences
    China University of Geosciences)

  • Chunhai Tan

    (China University of Geosciences
    China University of Geosciences)

  • Dongze Lei

    (China University of Geosciences
    China University of Geosciences)

  • Haixiang Guo

    (China University of Geosciences
    China University of Geosciences)

Abstract

Reliable prediction of reservoir displacement is essential for practical applications. Machine learning offers an attractive and accessible set of tools for the displacement prediction of reservoir landslides. In the present study, earthworm optimization algorithm-optimized support vector regression (EOA-SVR) was proposed for the reliable prediction of reservoir landslide displacement. The proposed approach was evaluated and compared with metaheuristics, including artificial bee colony (ABC), biogeography-based optimization (BBO), genetic algorithm (GA), gray wolf optimization (GWO), particle swarm optimization (PSO), and water cycle algorithm (WCA), by the Friedman and post hoc Nemenyi tests. The results from the Baishuihe landslide showed that the EOA-optimized SVR provided satisfactory performance with a Kling–Gupta efficiency (KGE) greater than 0.98 and nearly optimal values of the coefficient of determination. Significant performance differences were revealed between the compared metaheuristics. The EOA is superior with respect to both performance and stability. The hyperparameter sensitivity analysis demonstrated that the EOA can stably provide reliable predictions by maintaining the optimal solution. The experimental results from the Baishuihe landslide indicate that the EOA-optimized SVR is promising for accurate and reliable prediction of reservoir landslide displacements, thus aiding in medium- and long-term landslide early warning.

Suggested Citation

  • Zhiyang Liu & Junwei Ma & Ding Xia & Sheng Jiang & Zhiyuan Ren & Chunhai Tan & Dongze Lei & Haixiang Guo, 2024. "Toward the reliable prediction of reservoir landslide displacement using earthworm optimization algorithm-optimized support vector regression (EOA-SVR)," 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. 120(4), pages 3165-3188, March.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:4:d:10.1007_s11069-023-06322-1
    DOI: 10.1007/s11069-023-06322-1
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Xiaohua Zeng & Changzhou Liang & Qian Yang & Fei Wang & Jieping Cai, 2025. "Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-31, January.

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