Mohamed Mastir

Explainable machine learning for anticipating county-level disaster recovery burden in the built environment

This study aims to develop and evaluate an explainable machine-learning framework to anticipate county-level high recovery burden from natural disasters one year ahead using only public administrative and social-vulnerability data. Its substantive aim is to help built-environment decision makers shift from reactive...