Prediction of Peak ground acceleration for earthquakes by using intelligent methods

Peak ground acceleration (PGA) is equal to the maximum ground acceleration that occurred during earthquake shaking at a location and the design basis earthquake ground motion is often defined in terms of PGA. In this paper, three intelligent methods are proposed for predicting of PGA in regions where PGA value is greater than 0.5g. These knowledge base methods are Adaptive Network Based Fuzzy Inference System (ANFIS), Support Vector Regression Neural Network (SVRNN) and Radial Basis Function Network (RBFN). For this purpose, four key parameters of the past earthquakes are used, such as Moment Magnitude, Rupture distance, Site class and Style of faulting. The performed simulations determine the performance of the discussed networks and the obtained results of the methods by using Matlab Software show that SVRNN is better for PGA prediction in future earthquakes.

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