Journal of Shanghai Jiaotong University ›› 2018, Vol. 52 ›› Issue (10): 1388-1395.doi: 10.16183/j.cnki.jsjtu.2018.10.030

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Displacement Prediction of Landslide Based on Variational Mode Decomposition and AMPSO-SVM Coupling Model

U Feng,FAN Chunju,XU Xunjian,LI Li,NI Jiayun   

  1. 1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Disaster Prevention and Reduction Center, State Grid Hunan Electric Company, Changsha 410007, China; 3. School of Statistics, East China Normal University, Shanghai 200241, China

Abstract: Landslide is a natural disaster that seriously threatens and endangers the safety of life and property of residents. Landslide displacement prediction is helpful to predict natural disasters such as landslide. Monitoring data processing and the establishment of prediction model are the basis of landslide displacement prediction. According to the shortcomings of the EMD and EEMD decomposition algorithms which are applied widely in present analysis of time series,the variational mode decomposition algorithm is applied in signal processing to the landslide displacement sequences decomposition to obtain the subsequences. The AMPSO-SVM displacement prediction coupling model is constructed by combining adaptive mutation particle swarm optimization (AMPSO) and support vector machine (SVM). Apply the coupling model to predict displacement subsequences separately, then reconstruct all the sub-sequences prediction results and the total displacement prediction value is acquired. Taking the monitoring point XD1 in the Baishuihe landslide in the Three Gorges Reservoir area as an example, different scenarios are set to clarify the validity and stability of the proposed model. The case studies show that the prediction performance of landslide displacement based on variational mode decomposition and AMPSO-SVM coupling model is superior to BP neural network prediction model and SVM model optimized by the grid search algorithm, and it has a good theoretical basis and engineering application value.

Key words: varitional mode decomposition, landslide, displacement prediction, auto mutation particle swarm algorithm (AMPSO), support vector machine (SVM)

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