摘要
针对非线性波动性发展的滑坡,为了提高其位移变化的预测精度,以经验模态分解(Empirical Mode Decomposition)方法对滑坡监测地表位移的时间序列进行处理,将不规律变化的位移序列转化为存在一定规律变化的模态分量,得到不同频率的位移分量,对每一分量单独预测,避免误差相互影响,通过预测所有分量的变化趋势来综合预测位移序列的变化趋势,利用改进门限自回归模型(Threshold Auto Regressive)对非稳态谐波描述性较好的优势预测滑坡位移分量,最后模态叠加得到最终预测位移,建立了基于经验模态分解和门限自回归模型的组合预测模型,结合白水河滑坡实例数据验证该模型的预测精度,通过与BP神经网络模型、长短时间记忆网络模型进行预测对比,提出的组合模型预测精度较高,为滑坡位移的预测提供了一种新的方法。
This study aims to more accurately predict the displacement changes of landslides with nonlinear volatility development.The empirical mode decomposition is first employed to process the time series of monitoring surface displacement of a landslide, and then the irregularly changing displacement series is converted into modal components with regular changes, which generates displacement components at different frequencies.Each component is predicted separately so that the mutual influence of errors can be avoided.The comprehensive prediction of the changing trend of displacement series is based on the prediction of the changing trends of all components.The improved threshold autoregressive model able to well describe non-stationary harmonics is used to predict the landslide displacement components.Finally, the modal superposition yields the final predicted displacement.In this way, a combined prediction model based on empirical mode decomposition and threshold autoregressive model is established, and its prediction accuracy is verified with Baishuihe landslide data.Compared with a BP neural network model and a long short-term memory network model, the proposed model has a high prediction accuracy, which provides a new method for the prediction of landslide displacement.
作者
陈曦
高雅萍
涂锐
CHEN Xi;GAO Yaping;TU Rui(College of Earth Sciences,Chengdu University of Technology,Chengdu 610059,China;National Time Service Center,Chinese Academy of Sciences,Xi′an 710600,China)
出处
《人民珠江》
2022年第3期96-101,108,共7页
Pearl River
基金
四川省科技厅应用基础研究项目(2020YJ0362)
四川省测绘地理信息学会资助项目(CCX202114)。
关键词
滑坡位移预测
经验模态分解
门限自回归模型
组合预测
prediction of landslide displacement
empirical mode decomposition
threshold autoregressive model
combination prediction
