Abstract
This dissertation develops and tests a new data-driven framework for short-term roadway pluvial flash flood (PFF) risk estimation at the scale of road segments using crowdsourced navigation data and a simplified physics-based PFF model. Pluvial flash flooding (PFF) is defined as localized floods caused by an overwhelmed natural or engineered drainage system. This study develops a data curation and computational framework for data collection, preprocessing, and modeling to estimate the risk of PFF at road-segment scales. A hybrid approach is also developed that couples a statistical model and a simplified physics-based simulation model in a machine learning (ML) model to rapidly predict the risk of roadway PFF using Waze alerts in real-time.
Degree Date
Fall 12-17-2022
Document Type
Thesis
Degree Name
Ph.D.
Department
Civil and Environmental Engineering
Advisor
Barbara Minsker
Subject Area
Civil Engineering
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Safaei-Moghadam, Arefeh, "Leveraging Crowdsourced Navigation Data In Roadway Pluvial Flash Flood Prediction" (2022). Civil and Environmental Engineering Theses and Dissertations. 24.
https://scholar.smu.edu/engineering_civil_etds/24
