Abstract
ABSTRACTIn 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.
Highlights
Among all the natural calamities earthquakes are the most threatening natural calamity, due its tremendous destructive property
A prediction model based on SVR is proposed for forecasting peak ground acceleration (PGA), a parameter associated with seismic signals
The proposed prediction model could be used as a tool for faster and accurate prediction of the ground motion parameter with lesser calculation overhead, in all areas such as seismic risk assessment, seismic hazard analysis, earthquake resistant structural engineering, etc., where the principal ground motion parameters are used as a vital input parameter. c
Summary
Among all the natural calamities earthquakes are the most threatening natural calamity, due its tremendous destructive property. Alavi and Gandomi (2011) used a hybrid model ANN/SA (coupling of artificial neural network with simulated annealing) to predict the principal ground motion parameters PGA, PGV and PGD. Alavi et al (2011) developed a variant GMPE model for prediction of ground motion parameters using multi expression programming (MEP) This model gives comparatively reasonable prediction accuracy and validates the advantage of MEP over the traditional GMPE equations developed using regression analysis. This model develops the ground motion prediction equation considering the complex nature of the ground motion parameters, the model suffers the drawbacks of GP-based models as the functions are formed randomly and not on the physical process.
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