人民珠江2024，Vol.45Issue(2)：1-8,8.DOI:10.3969/j.issn.1001-9235.2024.02.001
基于时间序列与CNN-GRU的滑坡位移预测模型研究
Landslide Displacement Prediction Model Based on Time Series and CNN-GRU
摘要
Abstract
Landslide displacement prediction is an important basis for early landslide warning.This paper proposes a prediction model of landslide moving states based on time series and convolutional gated recurrent unit(CNN-GRU)to deal with the shortcomings of previous prediction models.Firstly,after employing wavelet analysis to determine the displacement of the trend term,the exponential smoothing method is adopted to decompose the cumulative displacement to obtain two displacement types of the trend term and the periodic term,and the trend term is fitted by a five-order polynomial.Then,the autocorrelation function is utilized to test the periodic displacement characteristics,and the gray correlation method is applied to determine the correlation degree between each factor and the periodic term.Meanwhile,the periodic term and the influencing factor are input into the CNN-GRU model for prediction,and finally the predicted cumulative displacement value is obtained by superposition.By taking the Baishui River landslide in the Three Gorges Reservoir area as an example,this paper selects the data from January 2004 to December 2012 for study,and the average absolute error percentage of the final prediction results is only0.525%,with RMSE of 9.614 and R2 of0.993.Experimental results show that CNN-GRU has higher prediction accuracy.关键词
位移预测/时间序列/卷积门控循环单元/白水河滑坡Key words
displacement prediction/time series/convolutional gated recurrent unit/Baishui River landslide分类
资源环境引用本文复制引用
符振涛,李丽敏,王莲霞,任瑞斌,崔成涛,封青青..基于时间序列与CNN-GRU的滑坡位移预测模型研究[J].人民珠江,2024,45(2):1-8,8.基金项目
国家自然科学基金项目(62203344) （62203344）
陕西省技术创新引导专项(2020CGXNG-009、2020CGXNX-009) （2020CGXNG-009、2020CGXNX-009）
陕西省自然科学基础研究计划(2022JM-322) （2022JM-322）
陕西省教育厅服务地方专项(2022JM-322) （2022JM-322）
