Location: Zoom*
Add to Calendar 2021-10-22T11:00:00 2021-10-22T13:00:00 America/New_York Doctoral Dissertation Proposal - Faria Tuz Zahura Advancing Modeling and Assessment of Recurrent Flooding Impacts to Coastal Urban Transportation Systems Committee Members: Dr. Donna Chen (Chair) - ESE Dr. Jon Goodall (Advisor) - ESE Dr. Teresa Culver - ESE Dr. Julie Quinn - ESE Dr. Matthew Reidenbach - Environmental Sciences Abstract: Zoom*

Advancing Modeling and Assessment of Recurrent Flooding Impacts to Coastal Urban Transportation Systems

Committee Members:

  • Dr. Donna Chen (Chair) - ESE
  • Dr. Jon Goodall (Advisor) - ESE
  • Dr. Teresa Culver - ESE
  • Dr. Julie Quinn - ESE
  • Dr. Matthew Reidenbach - Environmental Sciences

Abstract:

Recurrent flooding due to sea level rise and climate change is a growing concern for coastal communities. Forecasting when and where flooding might occur, at a spatial and temporal resolution that can best assist decision makers, is needed to improve flood resiliency within these coastal communities. Conventional physics-based models used by flood forecasters can simulate accurate flooding dynamics; however, their computational burden makes them unsuitable for hyper-resolution modeling in real-time. This research explores the potential of a machine learning method, Random Forest, for creating surrogate flood models suitable for real-time, hyper-resolution flood forecasting. Furthermore, new crowdsourced data products offer the potential to measure recurrent flooding impacts to transportation systems for communities across the globe. These data, due to the number of data collectors,  could offer coastal communities a systematic way to measure flooding impacts, helping communities to better understand changes over time and to identify streets vulnerable to recurrent flooding. With this motivation in mind, the first objective of the proposed dissertation is to create a surrogate flood model using Random Forest to efficiently forecast the depths and extent of pluvial flooding from a high-fidelity, physics-based model in an urban-coastal environment. The second objective is to build on the pluvial surrogate flood model created in Objective 1 to forecast the combined pluvial and tidal flooding in coastal communities. Finally, the third objective is to create a method for utilizing crowdsourced traffic data to identify roadways impacted by recurrent flooding based on traffic behavior, and to measure traffic delays due to flooding over time as sea level rise and climate change impacts increase. Across these three objectives, this dissertation research aims to advance coastal resiliency research and practice by creating new flood-predictive tools and methods for systematically measuring flood impacts using novel crowdsourced data.

*For Zoom information, please send an email to ese-programs@virginia.edu.