基于遗传模拟退火算法的滑坡位移预测
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中南大学

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Landslide displacement prediction based on Genetic Simulated Annealing algorithm
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Central South University

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    摘要:

    滑坡是一种常见的地质灾害,通常在复杂的地质条件下演化和发生,因此给社会和人类的生命财产安全造成了极大的危害。了解滑坡的发展规律,对灾害防治具有重要意义。在现有滑坡累积位移时间序列的基础上,提出了一种基于遗传模拟退火算法的滑坡位移预测方法。采用遗传模拟退火算法-BP神经网络对白水河滑坡预警区Z118观测点进行分析,利用前三个月的累积位移来预测第四个月的累积位移。分别与BP神经网络模型和Elman神经网络模型进行了比较。同时将遗传模拟退火算法的预测结果与支持向量机的结果进行了比较。研究结果表明,本文所建立的滑坡位移预测模型能有效地提高预测精度,为工程建设中的滑坡位移预测提供了参考。

    Abstract:

    Landslide is a common geological disaster, which usually evolves and occurs under complex geological conditions and brings great damage to the safety of human life and property. The understanding of the development of landslides is important for the prevention and control of disasters. Based on the field time series data of landslide cumulative displacement, a landslide displacement prediction method based on Genetic Simulated Annealing algorithm is proposed. The Genetic Simulated Annealing algorithm BP neural network is used to analyze the observation point Z118 in Baishui River landslide warning area, the cumulative displacement of the first three months is applied to predict the accumulated displacement of the fourth month. The results of BP neural network model and Elman neural network model are compared. At the same time, the prediction results of Genetic Simulated Annealing algorithm and Support Vector Machine are compared. The results show that the landslide displacement prediction model established in this article can effectively improve the prediction accuracy, and provide a reference for landslide displacement prediction in engineering construction.

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  • 收稿日期:2020-04-20
  • 最后修改日期:2020-08-14
  • 录用日期:2020-10-18
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