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The use of strongly biased data generally leads to large distortions in a trained machine learning model. We face this problem when constructing a predictor for earthquake-generated ground-motion intensity with machine learning. The machine learning predictor constructed in this study has an underestimation problem for strong motions, although the data fit on relatively weak ground motions is good. This underestimation problem is caused by the strong bias in available ground-motion records; there are few records of strong motions in the dataset. Therefore, we propose a hybrid approach of machine learning and conventional ground-motion prediction equation. This study demonstrates that this hybrid approach machine learning technology and physical model reduces the underestimation of strong motions and leads to better prediction than either of the individual approaches applied alone.

Hybrid predictor for ground-motion intensity with machine learning and conventional ground motion prediction equation