Landslide displacement prediction model based on causal analysis
The landslide displacement prediction model is of great significance for disaster prevention and loss assessment.Taking the Bazimen landslide as an example,A CEEMD-SIWOA-BP combined prediction model for landslide displacement considering time delay was established based on displacement monitoring data in this paper.Firstly,using the complete ensemble empirical mode decomposition(CEEMD)method,the displacement monitoring data is decomposed into multiple signal components and reconstructed into landslide trend and periodic terms;Then,a BP-SIWOA model was constructed,and Singer(SI)chaotic mapping and adaptive weight factors were introduced to improve the global search and convergence ability of the whale optimization algorithm(WOA).The improved whale optimization algorithm was used to assign connection weights and threshold terms to the BP neural network model,predict the trend term displacement using a cubic polynomial,and consider the time delay effect for the periodic displacement term.The causal relationship between rainfall,reservoir water level,and periodic displacement was analyzed and predicted using the convergent cross mapping(CCM)method;Finally,the cumulative prediction value of landslide displacement was obtained by overlaying the results of each component,and the prediction accuracy was evaluated.The results show that the performance of this method is superior to other models,verifying the feasibility of CEEMD-SIWOA-BP combined prediction considering time delay,and providing technical reference for landslide disaster warning and prediction.
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