大地测量与地球动力学2026，Vol.46Issue(6)：748-757,10.DOI:10.14075/j.jgg.2025.08.293
基于优化时序分解和特征选择的滑坡位移预测模型
Landslide Displacement Prediction Model Based on Optimized Time Series Decomposition and Feature Selection
摘要
Abstract
Aiming at the problem that it is difficult for the temporal decomposition model to accurately distinguish the effects of induced factors on different displacement components,and the prediction ac-curacy is insufficient under the uncertainty of meteorological data,a landslide displacement prediction network model based on optimized time series decomposition and feature selection is proposed.First-ly,the variational modal decomposition(GA-VMD)method optimized by singular spectral analysis(SSA)and genetic algorithm is combined with induced factors to decompose the landslide displace-ment.Subsequently,an improved Nishihara model with fusion inducible factors is constructed to pre-dict the trend term displacement,and the combined network of convolutional neural network and ga-ted recurrent unit(CNN-SE-GRU)combined with compression and excitation network was used to model the period term displacement,and the random term displacement is reconstructed through fre-quency domain analysis.Finally,the probability interval of the displacement prediction results is con-structed by combining kernel density estimation(KDE)and Monte Carlo simulation.Taking the Hei-fangtai landslide in Gansu province as an example,the RMSE and MAPE of the prediction model are 1.52 mm and 0.38％,respectively,and the prediction accuracy of the model is significantly improved compared with the traditional prediction model,providing more reliable technical support for landslide early warning.关键词
滑坡位移预测/时序分解/特征选择/神经网络/误差传播建模Key words
landslide displacement prediction/time series decomposition/feature selection/neural network/error propagation modeling分类
天文与地球科学引用本文复制引用
王东民,赵丽华,瞿伟,杭资牧,王利..基于优化时序分解和特征选择的滑坡位移预测模型[J].大地测量与地球动力学,2026,46(6):748-757,10.基金项目
国家重点研发计划(2024YFC3012603) （2024YFC3012603）
国家自然科学基金(42174006) （42174006）
陕西省杰出青年科学基金(2022JC-18). （2022JC-18）
