Abstract In order to accurately predict the landslides deformation trend, aiming at the complex nonlinear characteristics of loess landslide displacement change, we propose an Elman neural network model based on genetic algorithm and particle swarm optimization algorithm(GA-PSO-Elman). Considering the problem that Elman neural network may fall into local optimal solution due to the randomness of structural parameters, we combine the strong global search ability of genetic algorithm(GA) with the local search ability of particle swarm optimization(PSO) to optimize the weight and threshold of structural parameters of the prediction model, and improve the prediction accuracy and convergence speed. The model is applied to the deformation prediction of Heifangtai landslide in Linxia, Gansu Province. The results show that the new model has better accuracy and stability than traditional BP neural network and single Elman neural network. Considering many factors affecting landslide, and adding humidity and precipitation to each training model, we improve the learning speed and convergence speed of GA-PSO-Elman model, thus effectively improving the accuracy of deformation prediction.
WANG Zhibiao,ZHAO Lihua. Prediction of Loess Landslides Deformation Using Elman Neural Network Model Based on Genetic Algorithm and Particle Swarm Optimization[J]. jgg, 2023, 43(7): 679-684.
WANG Zhibiao,ZHAO Lihua. Prediction of Loess Landslides Deformation Using Elman Neural Network Model Based on Genetic Algorithm and Particle Swarm Optimization[J]. jgg, 2023, 43(7): 679-684.
