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Landslide displacement prediction based on combining method with optimal weight

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  • Xiuzhen Li
  • Jiming Kong
  • Zhenyu Wang

Abstract

Predicting the deformation and evolution tendency of landslides is essential to landslide disaster prevention and mitigation. At present, most of the proposed models for landslide displacement prediction belong to single models. It is difficult to accurately describe the deformation and evolution law only by a single model for the complexity of landslides and limitation of the models. In this paper, we presented an application of linear combination model with optimal weight in landslide displacement prediction. We took Huanlongxicun and Saleshan landslides in Gansu province of China as examples, firstly to build GM(1,1) and Verhulst models for displacement prediction of the two landslides; then build two linear combination models of the two landslides, on the basis of the combining theory with optimal weight and the prediction results of the GM(1,1) and Verhulst models. The results show that the prediction accuracies of the combining models are much higher than those of the single models for both Huanglongxicun landslide and Saleshan landslide. Therefore, the combining model with optimal weight is an effective and feasible method to further improve accuracy for landslide displacement prediction. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Xiuzhen Li & Jiming Kong & Zhenyu Wang, 2012. "Landslide displacement prediction based on combining method with optimal weight," 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. 61(2), pages 635-646, March.
    • Handle: RePEc:spr:nathaz:v:61:y:2012:i:2:p:635-646
    DOI: 10.1007/s11069-011-0051-y
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    References listed on IDEAS

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    1. P. Lu & M. Rosenbaum, 2003. "Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability," 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. 30(3), pages 383-398, November.
    2. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    3. Sornette, D & Helmstetter, A & Andersen, J.V & Gluzman, S & Grasso, J.-R & Pisarenko, V, 2004. "Towards landslide predictions: two case studies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(3), pages 605-632.
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    Citations

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    Cited by:

    1. Weidong Wang & Jiaying Li & Xia Qu & Zheng Han & Pan Liu, 2019. "Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in 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. 96(3), pages 1121-1139, April.
    2. Li, Shaohong & Wu, Na, 2021. "A new grey prediction model and its application in landslide displacement prediction," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    3. Hong Wang & Guangyu Long & Jianxing Liao & Yan Xu & Yan Lv, 2022. "A new hybrid method for establishing point forecasting, interval forecasting, and probabilistic forecasting of landslide displacement," 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. 111(2), pages 1479-1505, March.
    4. Liulei Bao & Guangcheng Zhang & Xinli Hu & Shuangshuang Wu & Xiangdong Liu, 2021. "Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function," Energies, MDPI, vol. 14(4), pages 1-24, February.
    5. Xinchang Liu & Bolong Liu, 2023. "A Hybrid Time Series Model for Predicting the Displacement of High Slope in the Loess Plateau Region," Sustainability, MDPI, vol. 15(6), pages 1-26, March.
    6. Zian Lin & Xiyan Sun & Yuanfa Ji, 2022. "Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    7. Xiaoyang Yu & Cheng Lian & Yixin Su & Bingrong Xu & Xiaoping Wang & Wei Yao & Huiming Tang, 2022. "Selective ensemble deep bidirectional RVFLN for landslide displacement prediction," 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. 112(1), pages 725-745, May.
    8. Zian Lin & Yuanfa Ji & Weibin Liang & Xiyan Sun, 2022. "Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model," Mathematics, MDPI, vol. 10(13), pages 1-19, June.

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