Jonathan L. Goodall

About

Jonathan L. Goodall is a Professor in the Department of Civil and Environmental Engineering at the University of Virginia and serves as the Director of the UVA Engineering Link Lab. His work focuses on water resources engineering, specifically advancing the field of hydroinformatics, where data and computational sciences are used to improve understanding, forecasting, and management of water systems. He collaborates with students in the Hydroinformatics Research Group to adapt techniques from artificial intelligence, machine learning, and cyber-physical systems for real-time flood mitigation and enhanced flood resiliency. Goodall is a registered Professional Engineer, a Fellow of the American Society of Civil Engineers, and an elected member of the Virginia Academy of Science, Engineering, and Medicine. His educational background includes a Ph.D. and M.S.E. in Civil Engineering from the University of Texas at Austin and a B.S. in Civil Engineering from the University of Virginia. His research interests encompass water resources engineering, hydroinformatics, stormwater flooding, smart cities, machine learning, and flood forecasting.

Research topics

• Computer Science
• Engineering
• Environmental science
• Geography
• Artificial Intelligence
• Meteorology
• Ecology
• Environmental planning
• Geotechnical engineering
• Political Science

Selected publications

• Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community

  Water Resources Research · 2020 · 181 citations

  Abstract Mitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective flood prediction for prompt action. Typically, physics‐based 1‐D pipe/2‐D overland flow models are used to simulate urban pluvial flooding. Because these models require significant computational resources and have long run times, they are often unsuitable for real‐time flood prediction at a street scale. This study explores the potential of a machine learning method, Random Fo…

  DOI

• Position paper: Open web-distributed integrated geographic modelling and simulation to enable broader participation and applications

  Earth-Science Reviews · 2020 · 135 citations

  Integrated geographic modelling and simulation is a computational means to improve understanding of the environment. With the development of Service Oriented Architecture (SOA) and web technologies, it is possible to conduct open, extensible integrated geographic modelling across a network in which resources can be accessed and integrated, and further distributed geographic simulations can be performed. This open web-distributed modelling and simulation approach is likely to enhance the use of e…

  DOI

• Exploring real-time control of stormwater systems for mitigating flood risk due to sea level rise

  Journal of Hydrology · 2020 · 89 citations

  DOI

• Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning

  Journal of Hydroinformatics · 2020 · 73 citations

  Senior authorCorresponding

  Abstract Flooding in coastal cities is increasing due to climate change and sea-level rise, stressing the traditional stormwater systems these communities rely on. Automated real-time control (RTC) of these systems can improve performance, and creating control policies for smart stormwater systems is an active area of study. This research explores reinforcement learning (RL) to create control policies to mitigate flood risk. RL is trained using a model of hypothetical urban catchments with a tid…

  DOI

• Estimating impacts of recurring flooding on roadway networks: a Norfolk, Virginia case study

  Natural Hazards · 2021 · 54 citations

  Senior authorCorresponding
  DOI

Recent grants

• Collaborative Research: CiC (SEA): Using the Cloud to Model and Manage Large Watershed Systems

  NSF · $28k · 2014–2015

• NRT: A Graduate Traineeship in Cyber Physical Systems

  NSF · $3.6M · 2018–2024

• Workshop for Coordination of OpenMI and HIS Development in England, spring 2008

  NSF · $19k · 2008–2009

Frequent coauthors

• Mohamed M. Morsy

  State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering

  114 shared

• Jeffrey M. Sadler

  Oklahoma State University

  92 shared

• Benjamin D. Bowes

  University of Virginia

  66 shared

• Anthony M. Castronova

  Consortium of Universities for the Advancement of Hydrologic Science

  56 shared

• Madhur Behl

  50 shared

• David G. Tarboton

  Utah State University

  50 shared

• Yawen Shen

  University of Virginia

  47 shared

• Faria Tuz Zahura

  Government of the United States of America

  40 shared

Labs

• Link LabPI

Awards & honors

• Fellow, American Society of Civil Engineers
• Early Career Research Excellence Award, International Enviro…
• CAREER Award, National Science Foundation (2009)
• Elected Member, Virginia Academy of Science, Engineering, an…

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