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Advance Landslide Prediction and Warning Model Based on Stacking Fusion Algorithm

Author

Listed:
  • Zian Lin

    (School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China)

  • Yuanfa Ji

    (Information and Communication School, Guilin University of Electronic Technology, Guilin 541004, China)

  • Xiyan Sun

    (Information and Communication School, Guilin University of Electronic Technology, Guilin 541004, China)

Abstract

In landslide disaster warning, a variety of monitoring and warning methods are commonly adopted. However, most monitoring and warning methods cannot provide information in advance, and serious losses are often caused when landslides occur. To advance the warning time before a landslide, an innovative advance landslide prediction and warning model based on a stacking fusion algorithm using Baishuihe landslide data is proposed in this paper. The Baishuihe landslide area is characterized by unique soil and is in the Three Gorges region of China, with a subtropical monsoon climate. Based on Baishuihe historical data and real-time monitoring of the landslide state, four warning level thresholds and trigger conditions for each warning level are established. The model effectively integrates the results of multiple prediction and warning submodels to provide predictions and advance warnings through the fusion of two stacking learning layers. The possibility that a risk priority strategy can be used as a substitute for the stacking model is also discussed. Finally, an experimental simulation verifies that the proposed improved model can not only provide advance landslide warning but also effectively reduce the frequency of false warnings and mitigate the issues of traditional single models. The stacking model can effectively support disaster prevention and reduction and provide a scientific basis for land use management.

Suggested Citation

  • Zian Lin & Yuanfa Ji & Xiyan Sun, 2023. "Advance Landslide Prediction and Warning Model Based on Stacking Fusion Algorithm," Mathematics, MDPI, vol. 11(13), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2833-:d:1178129
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    References listed on IDEAS

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    1. Xiang Wang & Shen Gao & Yibin Guo & Shiyu Zhou & Yonghui Duan & Daqing Wu & Ning Cai, 2022. "A Combined Prediction Model for Hog Futures Prices Based on WOA-LightGBM-CEEMDAN," Complexity, Hindawi, vol. 2022, pages 1-15, February.
    2. Xiangqian Wang & Ningke Xu & Xiangrui Meng & Haoqian Chang, 2022. "Prediction of Gas Concentration Based on LSTM-LightGBM Variable Weight Combination Model," Energies, MDPI, vol. 15(3), pages 1-17, January.
    3. Mohammed A. Bou-Rabee & Muhammad Yasin Naz & Imad ED. Albalaa & Shaharin Anwar Sulaiman, 2022. "BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones," Energies, MDPI, vol. 15(6), pages 1-12, March.
    4. 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.
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