Predicting peak inundation depths with a physics informed machine learning model.

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average of 0.

Buy them out before they are built: evaluating the proactive acquisition of vacant land in flood-prone areas.

Rising flood damages have prompted local communities to implement buyout and property acquisition programmes to eliminate repetitive losses for at-risk properties. However, buyouts are often costly to implement and are reactionary solutions to flooding. This study quantifies the benefits of acquiring vacant private properties in flood-prone areas rather than acquiring such properties after they are built up.