| 基于多重分形特征和分项组合预测联合响应的滑坡预警预测研究 |
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| 引用本文： | 雷 恒,周晓岚,王永强.基于多重分形特征和分项组合预测联合响应的滑坡预警预测研究[J].大地测量与地球动力学,2022,42(9):885-891. |
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| 作者姓名： | 雷 恒 周晓岚 王永强 |
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| 摘 要： | 为准确掌握滑坡变形发展规律，基于滑坡变形监测成果构建滑坡预警预测模型，即先利用MF-DFA模型开展滑坡变形数据的多重分形特征分析，并进一步利用M-K分析构建双重判据（Δa指标判据和Δf(a)指标判据）进行滑坡预警研究；另外，在利用集成经验模态分解法对滑坡变形数据信息进行分离处理基础上，通过GOA-RNN-CT模型实现滑坡变形的分项组合预测。结果表明，h(q)值随波动函数q值减小而减小，说明滑坡变形数据具有多重分形特征，且预警分级研究表明，滑坡预警等级为Ⅱ级，即滑坡变形趋向不利方向发展；同时，通过变形预测分析认为，分项组合预测在滑坡变形预测中具有较优的预测效果和稳定性，且外推预测结果显示，滑坡变形会继续增加；最后，将多重分形特征研究结果和变形预测分析结果进行联合响应综合得出，滑坡现有预警等级相对不利，且后续变形仍会进一步增加，趋向不稳定方向发展，建议对滑坡采取必要防治措施。
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| 关 键 词： | 滑坡 多重分形特征 变形预警 预测 联合响应 |
Research on Landslide Early Warning and Prediction Based on Combined Response of Multifractal Characteristics and Sub Item Prediction |
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| LEI Heng,ZHOU Xiaolan,WANG Yongqiang.Research on Landslide Early Warning and Prediction Based on Combined Response of Multifractal Characteristics and Sub Item Prediction[J].Journal of Geodesy and Geodynamics,2022,42(9):885-891. |
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| Authors: | LEI Heng ZHOU Xiaolan WANG Yongqiang |
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| Abstract: | In order to accurately grasp the development law of landslide deformation, based on landslide deformation monitoring results, we construct a landslide early warning prediction model. We first carry out multifractal characteristics analysis of landslide deformation data using the MF-DFA model, and use M-K analysis to study landslide early warning by constructing dual criteria, namely Δa index criterion and Δf(a) index criterion. Secondly, based on the separation and processing of landslide deformation data by ensemble empirical mode decomposition method, we use the GOA-RNN-CT model to realize the sub item combination prediction of landslide deformation. The results show that the value of h(q) decreases with the decrease of q value of wave function, indicating that landslide deformation data has multifractal characteristics. Through the study of early warning classification, we conclude that the landslide early warning level is grade II, that is, the landslide deformation tends to develop in an unfavorable direction. At the same time, through the deformation prediction analysis, we conclude that the sub item combination prediction has better effect and stability in landslide deformation prediction, and the extrapolation prediction results show that the landslide deformation will continue to increase. Finally, the combined response of multifractal characteristics results and deformation prediction analysis results show that the existing early warning level of landslide is relatively unfavorable, and the subsequent deformation will further increase and tend to further instability. We suggest taking necessary prevention and control measures for landslide. |
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| Keywords: | landslide multifractal characteristics deformation early warning prediction combined response |
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