Rapid on-site peak ground acceleration estimation based on support vector regression and P-wave features in Taiwan

This study extracted some P-wave features from the first few seconds of vertical ground acceleration of a single station. These features include the predominant period, peak acceleration amplitude, peak velocity amplitude, peak displacement amplitude, cumulative absolute velocity and integral of the squared velocity. The support vector regression was employed to establish a regression model which can predict the peak ground acceleration according to these features. Some representative earthquake records of the Taiwan Strong Motion Instrumentation Program from 1992 to 2006 were used to train and validate the support vector regression model. Then the constructed model was tested using the whole earthquake records of the same period as well as the 2010 Kaohsiung earthquake with 6.4 M_L. The effects on the performance of the regression models using different P-wave features and different length of time window to extract these features are studied. The results illustrated that, if the first 3s of the vertical ground acceleration was used, the standard deviation of the predicted peak ground acceleration error of the whole tested 15-years earthquake records is 20.89gal.The length of time window could be shortened, e.g. 1s, and the prediction error is slightly sacrificed, in order to prolong the lead-time before destructive S-waves reaches.