Intelligent Flood Detection and Warning System to Assist Homeless Communities and Emergency Management Entities
Lead PI:
Erfan Goharian

Unsheltered homelessness has grown at staggering rates, particularly across West Coast cities such as San Diego. Unsheltered people are at higher risk than the general population of experiencing flooding risk, as they are more likely both to be living in the most flood-vulnerable locations as well as disconnected from existing flood warning systems. This predicament results in unequal disasters and environmental impact, burdening the most vulnerable people with the least information in critical moments. From a technical aspect, the absence of an intelligent system for flood detection causes inaccuracy in prediction and delays in responses. This project aims to overcome these challenges by integrating imagery data into the current state-of-the-art flood data acquisition and urban infrastructure modeling. The broader impact of the study involves raising community awareness about the use of new smart technologies and supporting proactive flood and emergency management by engaging residents, businesses, and practitioners in the development of the research program. Involved students will have the opportunity to learn multi-disciplinary research topics through engaging in collaborative teamwork. The new smart system and connected decision-making framework will be transferable to other flood-prone communities across the U.S. West and East Coasts that are confronting more frequent floods as a result of climate change coinciding with an unprecedented housing affordability crisis. This project will create a diverse, multidisciplinary community of researchers, practitioners, and concerned citizens to develop novel technologies and integrated theories and methods to improve the time and accuracy of flood data acquisition, detection, and monitoring. A set of deep learning-based image processing systems will be developed and trained using large fully segmented flood image datasets to detect formation and monitor flood events. A flood model will be developed to simulate and forecast real-time floods and impacts. This project will enhance the response time and accuracy of flood early warning and monitoring systems to support the resilience and emergency management of smart and connected coastal communities. During the planning phase, a Community Advisory Group will be assembled and convened to advise on the development of the intelligent flood system and guide the human subject’s data collection activities, including structured interviews with unsheltered people. This project enhances the knowledge needed to support smart and connected communities by including 1) novel data collection techniques from various sources of information, such as ground-based cameras, 2) artificial intelligence-based flood visual sensing and analyzing diverse data, and 3) substantive community engagement that centers the needs of unsheltered people.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Erfan Goharian
Dr. Erfan Goharian’s research focuses on water resources systems analysis and integrated management of water resources. He develops complex quantitative and computational models with the purpose of providing enhanced knowledge needed to better understand interactions in coupled human-natural systems and water-energy-food nexus, and how they are shaped by climate, environmental, economic, social and political changes. Before joining University of South Carolina, he was leading the research on re-operation of integrated water systems in California as a part of University of California Water Security and Sustainability Research Initiative (UC Water). Dr. Goharian holds a Ph.D. degree in Civil and Environmental Engineering with emphasize on Water Resources Management from the University of Utah. Beyond his technical background, he has experience working in collaborations across institutions and disciplinary boundaries.
Performance Period: 08/01/2023 - 07/31/2024
Institution: University of South Carolina at Columbia
Award Number: 2244837