Displacement prediction of step-like landslide based on temporal analysis and CNN-BiLSTM-AM
Traditional models based on recurrent neural networks have insufficient predictive capabilities for stepwise landslide displacement.To address this issue,a dynamic landslide displacement prediction model based on time series analysis and CNN-BiLSTM-AM is proposed.Firstly,the sequence is decomposed into trend components,periodic components,and random components using the variational mode decomposition(VMD)method.The trend component displacement is fitted using the second-order exponential smoothing method.Then,the maximum mutual information coefficient(MIC)method is introduced to calculate the correlation between various influencing factors and periodic component displacement.For both periodic and random component displacements,a hybrid CNN-BiLSTM-AM model is used for multi-factor and single-factor prediction.Finally,the predicted values of each component are accumulated to obtain the cumulative displacement prediction results.Experimental results show that the proposed method achieves fitting coefficients R2 of 0.984 and 0.987 in the final cumulative displacement prediction results,with average absolute errors(MAE)of 5.334 and 3.947,and root mean square errors(RMSE)of 6.196 and 4.941,respectively.Compared to CNN-LSTM,SSA-KELM,and NARX methods,the proposed method better captures the temporal correlations in monitoring data,significantly improving prediction accuracy,and providing valuable references for stepwise landslide early warning and mitigation efforts.
step-like landslidevariational mode decompositionattention mechanismconvolutional neural networkbi-directional long short-term memory
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