[4Rin1] Interactive 2

[4Rin1-94] Construction of explainable random forest predictor for ground-motion intensity

〇Hisahiko Kubo¹, Takashi Kunugi¹, Wataru Suzuki¹, Takeshi Kimura¹, Shin Aoi¹ (1.National Research Institute for Earth Science and Disaster Resilience)

Keywords:Predictor for ground-motion intensity, Random forest, XAI

We try to explain and interpret the random-forest predictor of earthquake ground-motion intensity and its prediction results. This study demonstrates that although the relationship of ground-motion intensity with earthquake magnitude or epicentral distance in the machine-learning model is consistent with the knowledge of seismology, the effects of source depth and site condition on ground-motion intensity in the machine-learning model are more complex than the assumptions of previous studies. The visualization of weak learners of the random forest indicates that its prediction is largely affected by the biased distribution of training data-set.