Abstract
This paper presents a hybrid least squares support vector machine (LSSVM) and particle swarm optimization (PSO) techniques to predict the slope stability. A modified PSO algorithm is employed in selecting the optimal values of the LSSVM parameters to improve the forecasting accuracy. Two examples of slope stability are presented to validate the predictive capability of the PSO-LSSVM model presented in this paper. A detailed sensitivity analysis is designed and performed to find the optimum values of parameters of PSO in these two examples. The results show that the proposed PSO-LSSVM model is a feasible and efficient tool for predicting the slope stability with high accuracy.
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