Modeling protective action decision-making in earthquakes by using explainable machine learning and video data
Xiaojian Zhang,
Xilei Zhao,
Dare Baldwin
et al.
Abstract:Earthquakes pose substantial threats to communities worldwide. Understanding how people respond to the fast-changing environment during earthquakes is crucial for reducing risks and saving lives. This study aims to study people’s protective action decision-making in earthquakes by leveraging explainable machine learning and video data. Specifically, this study first collected real-world CCTV footage and video postings from social media platforms, and then identified and annotated changes in the environment and… Show more
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