基于时间序列与时间卷积网络的滑坡位移预测
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P642.22

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成都信息工程大学四川省教育厅人文社科重点研究基地—气象灾害预测预警与应急管理研究中心2022年一般项目:基于时间卷积和迁移学习的滑坡位移预测方法研究(ZHYJ22-YB03),四川省科技厅应用基础研究项目(2021YJ0335)


A model for predicting landslide displacement based on time series and time convolution network
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    摘要:

    滑坡位移预测作为滑坡监测预警的重要组成部分,对滑坡灾害的防治具有重要意义。目前,滑坡位移预测大多集中循环架构的神经网络模型上,其存在梯度爆炸、消失问题等问题。为此,提出了一种基于时间序列与时间卷积网络(TCN)的滑坡位移预测模型。该模型通过移动平均法将滑坡位移分解为趋势项位移和周期项位移。其次,采用Holt线性趋势模型预测趋势项位移,并建立时间卷积网络预测周期项位移。最后,将趋势项位移和周期项位移叠加,实现滑坡位移的预测。将该模型用于八字门滑坡的观测研究,结果表明:该模型相较于循环架构的神经网络模型更能有效的提取时序特征,预测精度更高。将基于TCN的滑坡位移预测模型应用于滑坡位移预测具有广阔的应用前景。

    Abstract:

    As an important part of landslide monitoring and early warning, landslide displacement prediction is of great significance to landslide disaster prevention. At present, the prediction of landslide displacement is mostly concentrated on the neural network model of circular structure, which has the problems of gradient explosion and disappearance. Therefore, a landslide displacement prediction model based on time series and time convolution network ( TCN ) is proposed. The model decomposes landslide displacement into trend displacement and periodic displacement by moving average method. Secondly, Holt linear trend model is used to predict trend term displacement, and time convolution network is established to predict periodic term displacement. Finally, the trend displacement and periodic displacement are superimposed to predict landslide displacement. The model is applied to the observation and study of Bazimen landslide. The results show that the model is more effective in extracting time series characteristics and higher prediction accuracy than the neural network model with cyclic architecture. The application of TCN-based landslide displacement prediction model to landslide displacement prediction has broad application prospects.

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江文金,冷小鹏,林祥,等. 基于时间序列与时间卷积网络的滑坡位移预测[J]. 科学技术与工程, 2023, 23(9): 3672-3679.
Jiang Wenjin, Leng Xiaopeng, Lin Xiang, et al. A model for predicting landslide displacement based on time series and time convolution network[J]. Science Technology and Engineering,2023,23(9):3672-3679.

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历史
  • 收稿日期:2022-08-27
  • 最后修改日期:2023-01-18
  • 录用日期:2022-12-02
  • 在线发布日期: 2023-04-12
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