A Time Series Decomposition Method for Landslide Displacement Based on Sliding Detection Algorithm
To address the issue of weak mechanical interpretation in the time-series decomposition model of step-type landslide displacement,we propose a decomposition method incorporating sliding Rnl step-point detection and improved weighted moving average method to modify step-term displacement.Both the Nishihara creep constitutive model and a self-adaptive improved genetic algorithm model were utilized.The proposed method was applied to de-compose the displacement time series of Baishuihe landslide.The results of the proposed method were compared with those of the MK Test,sliding t test,and the Bayes test,demonstrating that the sliding Rnl step-point detection yields more accurate and applicable results.Furthermore,the displacement time series decomposition results were also compared with those obtained from quadratic moving average time series decomposition,cubic exponential smoothing time series decomposition,and VMD time series decomposition.The findings reveal that our proposed de-composition method effectively addresses irregular displacement and enhances the mechanical interpretation of the landslide trend term.Additionally,the introduction of the most critical step-term displacement in landslide displace-ment prediction enhances the specificity of analysis and prediction.In conclusion,our decomposition model holds significant engineering value and serves as a valuable reference for time series prediction.
landslide displacementtime series decompositionstep term displacementcreep modelgenetic algorithmslide test
国家科技期刊平台
NSTL
万方数据
