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This study tries to develop new attenuation relationships of peak groundvelocity using machine learning methods; random forest and neural network. Inorder to compare with the predictors obtained by machine learning, we have alsoconstructed a new attenuation relationship of peak ground velocity usingthree-stage regression procedure proposed by Molas and Yamazaki (1995). In thisstudy, 6,944 ground motion records at 1,184 seismic observation stations whichwere observed during the 32 earthquakes are employed to construct theattenuation relationships. Ground motion records from the 4 recent earthquakesare used as the test dataset. The test results show that when the shortestdistance from the fault is small, the predictions by machine learningtechniques are more accurate than the traditional equation. However, there isstill a problem of overestimation in the predictors of machine learning, evenif weights are added to the training dataset. In addition, the stationcorrection factors based on machine learning were derived and proved to becorrelated with the average shear wave velocity.

Construction of Attenuation Relationship of Peak Ground Velocity Using Machine Learning and Examination of Station Correction Factor