Prediction of peak ground acceleration using ϵ-SVR, ν-SVR and Ls-SVR algorithm

Abstract

In this paper, a prediction model is developed using support vector machine for forecasting the parameter associated with ground motion of a seismic signal. The prediction model is developed using three learning algorithms, ϵ-support vector regression, ν-support vector regression and least square-support vector regression (Ls-SVR) for forecasting peak ground acceleration (PGA), a parameter associated with ground motion of a seismic signal. The prediction model is developed for each of the algorithms with different kernel functions, namely linear kernel, polynomial kernel and radial basis function kernel. The ground motion parameter is related to four seismic parameters, namely faulting mechanism, average soil shear wave velocity, earthquake magnitude and source to site distance. The database used for modelling is NGA flatfile released by Pacific Earthquake Engineering Research Center. The experimental results show that the optimal prediction model for forecasting PGA is Ls-SVR with RBF kernel. It is observed that the developed prediction model is better compared to the existing conventional models and benchmark models in the same database. This paper further compares the three variations of SVR algorithm for ground motion parameter prediction model. The learning effectiveness of each algorithm is measured in terms of accuracy, testing error and overfitness measure.

Keywords

• pga
• earthquake prediction
• support vector regression
• seismic risk
• ls-svr