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Flood Inundation Mapping Using Machine Learning for Sustainable vs. Resilient Design

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Created:	Sep 25, 2025 at 1:09 p.m. (UTC)
Last updated:	Sep 28, 2025 at 4:38 a.m. (UTC)
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Abstract

This module focuses on teaching the knowledge and technical skills related to flood inundation mapping and its impact on designing resilient and sustainable hydraulic infrastructure. It consists of the following sections:

Section 1: Introduction
Section 2: Machine Learning for Flood Inundation Mapping
Section 3: Evaluation of Flood Inundation Mapping
Section 4: Decision Making for Hydraulic Design

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Name	Value
page_url	https://edx.hydrolearn.org/courses/course-v1:NMSU+CE483+Fall2024/about
thumbnail_url	https://ciroh-portal-static-data.s3.us-east-1.amazonaws.com/ffdae371-25b6-40b8-affd-52208b79dc6d.png

Credits

Funding Agencies

This resource was created using funding from the following sources:

Agency Name	Award Title	Award Number
National Oceanic and Atmospheric Administration (NOAA), University of Alabama	CIROH: Enabling collaboration through data and model sharing with CUAHSI HydroShare	NA22NWS4320003 to University of Alabama, subaward A23-0266-S001 to Utah State University

How to Cite

Cho, H., F. Ashraf, K. Dahal (2025). Flood Inundation Mapping Using Machine Learning for Sustainable vs. Resilient Design, HydroShare, http://www.hydroshare.org/resource/7dd0e38623ab436bba02076e20dccff4

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/

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