王佳, 朱鸿鹄, 叶霄, 等. 2022. 考虑时滞效应的库区滑坡位移预测——以新铺滑坡为例[ J]. 工程地质学报, 30(5): 1609-1619. doi: 10.13544/j.cnki.jeg.2022-0515.
    引用本文: 王佳, 朱鸿鹄, 叶霄, 等. 2022. 考虑时滞效应的库区滑坡位移预测——以新铺滑坡为例[ J]. 工程地质学报, 30(5): 1609-1619. doi: 10.13544/j.cnki.jeg.2022-0515.
    Wang Jia, Zhu Honghu, Ye Xiao, et al. 2022. Prediction of reservoir landslide displacements considering time lag effect-A case study of the Xinpu Landslide in the Three Gorges Reservoir area, China[J]. Journal of Engineering Geology, 30(5): 1609-1619. doi: 10.13544/j.cnki.jeg.2022-0515.
    Citation: Wang Jia, Zhu Honghu, Ye Xiao, et al. 2022. Prediction of reservoir landslide displacements considering time lag effect-A case study of the Xinpu Landslide in the Three Gorges Reservoir area, China[J]. Journal of Engineering Geology, 30(5): 1609-1619. doi: 10.13544/j.cnki.jeg.2022-0515.

    考虑时滞效应的库区滑坡位移预测——以新铺滑坡为例

    PREDICTION OF RESERVOIR LANDSLIDE DISPLACEMENTS CONSIDERING TIME LAG EFFECT—A CASE STUDY OF THE XINPU LANDSLIDE IN THE THREE GORGES RESERVOIR AREA, CHINA

    • 摘要: 库区滑坡失稳每年不同程度影响区内人民生活和生产安全,滑坡位移精准预测对于灾害风险预警及防灾减灾十分重要。常规的位移预测方法未充分考虑降雨、库水位波动等诱发因素对滑坡变形的时滞效应,无法精确识别滞后天数及各因素的影响程度,制约了预测精度的提高。本文以三峡库区新铺滑坡为例,根据2021年度的位移监测与水文气象数据集,利用皮尔逊相关系数法定量描述了山坡尺度上降雨、库水位波动对滑坡变形的时滞效应,结合BP神经网络建立了一种考虑时滞效应的滑坡位移预测模型。分析结果表明:在山坡尺度上,库水位波动对地表变形的时滞效应明显,滞后时间呈现出从近岸向远岸逐渐增加的规律;降雨量对地表变形的时滞效应较弱,在山坡尺度上呈现相关度不高、滞后天数较短的规律;与未考虑时滞因素的模型相比,本研究中的滑坡位移预测模型拟合优度提升了55.77%,均方根误差降低了31.60%,模型预测精度显著提高。研究成果一定程度上揭示了特大型库区滑坡的变形机理,并为同类滑坡的位移精准预测提供了参考依据。

       

      Abstract: Landslide instability in the reservoir area usually disproportionately impacts on the safety of life and production. Reliable landslide displacement prediction is of great importance for risk warning and disaster prevention and mitigation. However, conventional displacement prediction models fail to consider the lag effect of landslide deformation induced by the controlling factors(rainfall and reservoir water level), and to determine the lag time and the degree of influence. This paper takes the Xinpu landslide in the Three Gorges reservoir area as an example. The lag effect of landslide deformation induced by rainfall and reservoir water level at the hillslope scale is quantitatively described using Pearson correlation coefficient method based on the displacement monitoring and hydro-meteorological dataset in 2021. A novel landslide displacement prediction method considering the lag effect is presented using BP neural network model. The results show that the time lag effect of surface deformation induced by reservoir water level changes is obvious on the hillside scale. The lag time shows a pattern of increasing from near shore to far shore. The time lag effect induced by rainfall on surface deformation is weaker, and shows a pattern of low correlation with shorter lag time. Compared with the prediction model without considering the time lag effect, the fit of the model accounting for time lag effect is improved by 55.77%, and the root mean square error is reduced by 31.5%. The research results reveal the deformation mechanism of large-scale reservoir landslides to a certain extent, which can provide a reference for displacement prediction of similar landslides.

       

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