GeoAI-Driven Flood Risk Modeling: Integrating Hazard, Exposure, and Vulnerability
Day 3 Session 2 (1:30PM)
Presenters:
Hemal Dey (University of Alabama)
Flood risk modeling plays a central role in mitigation planning and operational preparedness. In this hands-on workshop, participants will develop a GeoAI workflow that integrates hazard, exposure, and vulnerability data to produce predictive flood-risk maps.
Participants will work with historical flood-damage data, geospatial predictors, and machine-learning models such as Random Forests. The session also introduces explainable AI (XAI) techniques (e.g., SHAP) to identify key drivers of flood risk and demonstrates how results can be visualized using GIS platforms. Emphasis is placed on building reproducible modeling pipelines that support operational decision-making, risk prioritization, and emergency planning workflows.
Learning Outcomes:
- Prepare geospatial hazard, exposure, and vulnerability datasets for flood-risk modeling
- Train machine-learning models for flood-risk prediction
- Interpret model outputs using explainable AI methods
- Produce flood-risk maps for planning and operational applications