PREDICTION OF EARTHQUAKE INDUCED DISPLACEMENTS OF SLOPES USING HYBRID SUPPORT VECTOR REGRESSION WITH PARTICLE SWARM OPTIMIZATION

Displacements induced by earthquake can be very large and result in severe damage to earth and earth supported structures including embankment dams, road embankments, excavations and retaining walls. It is important, therefore, to be able to predict such displacements. In this paper, a new approach to prediction of earthquake induced displacements of slopes (EIDS) using hybrid support vector regression (SVR) with particle swarm optimization (PSO) is presented. The PSO is combined with the SVR for determining the optimal value of its user-defined parameters. The optimization implementation by the PSO significantly improves the generalization ability of the SVR. In this research, the input data for the EIDS prediction consist of values of geometrical and geotechnical input parameters. As an output, the model estimates the EIDS that can be modeled as a function approximation problem. A dataset that includes 45 data points was applied in current study, while 36 data points (80%) were used for constructing the model and the remainder data points (9 data points) were used for assessment of degree of accuracy and robustness. The results obtained show that the SVR-PSO model can be used successfully for prediction of the EIDS.