北京测绘2025，Vol.39Issue(4)：554-560,7.DOI:10.19580/j.cnki.1007-3000.2025.04.025
基于CEEMDAN的深度学习滑坡位移组合预测模型
A combined landslide displacement prediction model based on CEEMDAN and deep learning
摘要
Abstract
This paper presented a combined landslide deep learning prediction model that integrated time series decomposition and component reconstruction.First,the isolated forest algorithm was used to remove outliers from the monitoring data,and the data's stationarity,autocorrelation,and normality were analyzed.Next,the adaptive noise complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method was introduced to decompose the landslide monitoring data into multiple independent time series components.Finally,prediction models are developed for different frequency components,and the prediction results are reconstructed.Experiments were conducted using landslide data samples collected from the Beidou satellite system(BDS)/global navigation satellite system(GNSS).The results show that the R² value of the proposed combined prediction model is improved by 60.66%and 50.77%compared to the single model and the decomposition model,respectively.The mean-root-square error SRMSE is reduced by 95.42%and 94.39%,and the mean absolute error SMAE is reduced by 95.69%and 96.74%,respectively.关键词
自适应噪声完备集合经验模态分解(CEEMDAN)/样本熵/深度学习/滑坡位移预测Key words
complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)/sample entropy/deep learning/landslide displacement prediction分类
天文与地球科学引用本文复制引用
舒玉平,徐金浩..基于CEEMDAN的深度学习滑坡位移组合预测模型[J].北京测绘,2025,39(4):554-560,7.基金项目
浙江省2023年度自然资源科技项目(2023-33) （2023-33）
