Authors: Huidae Cho, Fahmidah Ashraf, Kshitij Dahal – New Mexico State University
Title: HydroLearn Module: CIROH CE483 – Flood Inundation Mapping Using Machine Learning for Sustainable vs. Resilient Design
Presentation Type: Lightning Talk
Abstract: This lightning talk presents an innovative online module that empowers civil engineering students to apply machine learning techniques for flood inundation mapping. Using a real-world bridge collapse case in Bel Air North, Maryland, participants learn how to obtain FEMA flood hazard data, train and validate machine learning models, and compare their outputs with results from traditional hydrologic models. The module guides learners in applying these methods to inform decisions about sustainable versus resilient infrastructure design. Designed for senior undergraduates and graduate students, this self-paced module provides hands-on experience using accessible tools like Google Colab, bridging the gap between theoretical concepts and practical applications in hydraulic engineering.