基于优化时序分解和特征选择的滑坡位移预测模型OA

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.