Landslide displacement CEEMD-CIWOA-BP prediction model
To intuitively determine the causal relationship between landslide factors and period term displacements,and to improve the accuracy of the landslide displacement prediction model.The CEEMD-CIWOA-BP landslide displacement prediction model considering time lag was established by taking landslide displacement monitoring data of a mine as an example.Firstly,the landslide displacement monitoring data were decomposed into multiple signal components and res components by the CEEMD method,and reconstructed into landslide trend term and period term displacements.Then,the Cubic chaotic mapping and inertia weight factor were introduced to optimize the WOA algorithm,and the optimized WOA algorithm was used to assign values to the connectivity weight and bias term of the BP neural network model.Considering the time lag effect of rainfall and reservoir water level on landslide displacement,the Granger causality test was used to determine the causal relationship between rainfall and reservoir water level and cycle displacement,and the MIC method was used to determine the number of time lag periods,and the cycle displacement was predicted using the CIWOA-BP model.Finally,the results of the components were superimposed to obtain the cumulative predicted value of the landslide displacement,and the prediction accuracy of the model was evaluated.The results show that the performance of the CEEMD-CIWOA-BP model proposed in this paper was better than that of other models,and the feasibility of the proposed model was verified.The model proposed in this paper can provide certain reference value for landslide disaster early warning and prediction.
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