Acta Scientiarum Naturalium Universitatis Sunyatseni ›› 2020, Vol. 59 ›› Issue (5): 57-65.doi: 10.13471/j.cnki.acta.snus.2019.07.27.2019B071

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Displacement prediction model of Egongdai landslide in Lechangxia based on PSO-SVM algorithms

XU Wenbing1WANG Guohe2WANG Sheng1WU Mingzhao1YAO Qinghe1   

  1. 1. Department of Applied Mechanics and EngineeringSun Yat-sen UniversityGuangzhou 510006China

    2. China Nuclear Power Technology Research Institute Co. LtdShenzhen 518040China

  • Received:2019-07-27 Online:2020-09-25 Published:2020-09-25

Abstract: Taking Lechangxia Egongdai landslide as the research object,the influence of daily rainfall and osmotic pressure on slope deformation is considered. By establishing BP,SVM,PSO-BP,PSOSVM four landslide body deformation prediction models,the research data of the last 4 years is derived from the Lechangxia safety inspection system,and 410 sets of data are used for training through screening,and 30 sets of deformation displacements are taken as an output,after analysis,the PSO-SVM model is found to be the accurate model. Taking the PSO-SVM model as the basic model,the factors such as the number of iterations of the particle swarm algorithm,the population size,and velocity position correlation coefficient(k)are studied,and the best PSO-SVM is obtained when the three are 100,30,and 0.5,respectively. In this model,the RMSE,MAPE,and R2 are 0. 202 mm,0. 589%,and 0. 985,respectively. Compared with traditional methods such as large-scale finite element simulation software and multiple linear regression models,the prediction model proposed in this article can reduce the computational cost and obtain better processing results in the face of nonlinear problems. At the same time,it can reduce the lack of fitting accuracy caused by incomplete factor analysis.

Key words: Egongdai landslide, displacement prediction, model of PSO-SVM, parameter optimization

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