  This study tries to develop new attenuation relationships of peak ground
velocity using machine learning methods; random forest and neural network. In
order to compare with the predictors obtained by machine learning, we have also
constructed a new attenuation relationship of peak ground velocity using
three-stage regression procedure proposed by Molas and Yamazaki (1995). In this
study, 6,944 ground motion records at 1,184 seismic observation stations which
were observed during the 32 earthquakes are employed to construct the
attenuation relationships. Ground motion records from the 4 recent earthquakes
are used as the test dataset. The test results show that when the shortest
distance from the fault is small, the predictions by machine learning
techniques are more accurate than the traditional equation. However, there is
still a problem of overestimation in the predictors of machine learning, even
if weights are added to the training dataset. In addition, the station
correction factors based on machine learning were derived and proved to be
correlated with the average shear wave velocity.
