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Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique

Author

Listed:
  • Junwei Ma

    (Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Xiao Liu

    (Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Xiaoxu Niu

    (Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Yankun Wang

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Tao Wen

    (School of Geosciences, Yangtze University, Wuhan 430100, China)

  • Junrong Zhang

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Zongxing Zou

    (Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

Abstract

Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, QRNNs serve as base learning algorithms to generate multiple base learners. Final ensemble prediction is obtained by integration of all base learners through a probability combination scheme based on KDE. The Fanjiaping landslide in the Three Gorges Reservoir area (TGRA) was selected as a case study to explore the performance of the ensemble prediction. Based on long-term (2006–2018) and near real-time monitoring data, a comprehensive analysis of the deformation characteristics was conducted for fully understanding the triggering factors. The experimental results indicate that the QRNNs-KDE approach can perform predictions with perfect performance and outperform the traditional backpropagation (BP), radial basis function (RBF), extreme learning machine (ELM), support vector machine (SVM) methods, bootstrap-extreme learning machine-artificial neural network (bootstrap-ELM-ANN), and Copula-kernel-based support vector machine quantile regression (Copula-KSVMQR). The proposed QRNNs-KDE approach has significant potential in medium-term to long-term horizon forecasting and quantification of uncertainty.

Suggested Citation

  • Junwei Ma & Xiao Liu & Xiaoxu Niu & Yankun Wang & Tao Wen & Junrong Zhang & Zongxing Zou, 2020. "Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique," IJERPH, MDPI, vol. 17(13), pages 1-23, July.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:13:p:4788-:d:379897
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    References listed on IDEAS

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    1. Chen, Kunlong & Jiang, Jiuchun & Zheng, Fangdan & Chen, Kunjin, 2018. "A novel data-driven approach for residential electricity consumption prediction based on ensemble learning," Energy, Elsevier, vol. 150(C), pages 49-60.
    2. Yankun Wang & Huiming Tang & Tao Wen & Junwei Ma, 2020. "Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines," Complexity, Hindawi, vol. 2020, pages 1-15, May.
    3. Junwei Ma & Xiaoxu Niu & Huiming Tang & Yankun Wang & Tao Wen & Junrong Zhang, 2020. "Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach," Complexity, Hindawi, vol. 2020, pages 1-15, January.
    4. Xie Hu & Roland Bürgmann & William H. Schulz & Eric J. Fielding, 2020. "Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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    Cited by:

    1. Zongxing Zou & Sha Lu & Fei Wang & Huiming Tang & Xinli Hu & Qinwen Tan & Yi Yuan, 2020. "Application of Well Drainage on Treating Seepage-Induced Reservoir Landslides," IJERPH, MDPI, vol. 17(17), pages 1-20, August.
    2. Emily Ying Yang Chan & Holly Ching Yu Lam, 2021. "Research in Health-Emergency and Disaster Risk Management and Its Potential Implications in the Post COVID-19 World," IJERPH, MDPI, vol. 18(5), pages 1-3, March.

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