Problem Statement

In the U.S., the hydraulic collapse frequency is estimated at 0.02% per year for a total population of about 504,000 bridges. One such collapse occurred at the intersection of Bear Cabin Branch and Grafton Shop Road (Latitude 39.54491 and Longitude -76.3924) in the City of Bel Air North, MD. As a hydrologic and hydraulic engineer, you are tasked with making a decision on hydraulic design to create a more resilient and sustainable bridge in the future. The goal is to utilize a flood inundation map generated through machine learning to aid in this process. You will need to create a flood inundation map for the collapse site using machine learning techniques, and then compare and evaluate this map against existing physically based models. These flood inundation maps will be utilized in determining specific design elements for the new hydraulic bridge.

Module Overview

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