Design and Development of a Near Real-Time Community Crowdsourced Resilience Information System for Enhancing Community Resilience in the Face of Flooding and other Extreme Events
Lead PI:
Barnali Dixon

Communities along the coast are increasingly vulnerable to coastal hazards such as flooding due to extreme weather events and sea level rise. In the US alone, 40% of the population lives in coastal cities and subjected to elevated risks of such hazards. The probability of a flooding event in these communities is also increasing with global warming. The proposed project will design a neighborhood-scale Community Resilience Information System (CRIS-HAZARD) by leveraging citizen science and community participation for enabling real-time data-driven decision-making to make communities more resilient to flooding. CRIS-HAZARD will support frequent bi-directional flow of information among communities, research scientists, and decision-makers. The objective is to develop a platform that facilitates the smart and connected city framework by engaging diverse communities to improve the lives of all citizens, especially those who are marginalized. The project is piloted in Pinellas County, Florida, in the Tampa Bay region on the Gulf Coast of west-central Florida. This region’s geography and low elevation make it especially vulnerable to climate change-induced extreme weather events like flooding.

Unlike previous attempts at integrating data and models to predict flooding events, the approach of CRIS-HAZARD is distinctive as this research pioneers the integration of user-supplied data (crowd-sourced and social media) with real-time flood prediction models and uncertainty analysis techniques, which is expected to advance our understanding of risk and resilience in coastal communities facing persistent flooding events. The initiative integrates the expertise of research institutions, government agencies (Office of Emergency Management or OEM), local stakeholders, and community engagement networks to enhance community-based planning and policy decisions, promoting community resilience. Furthermore, the project fosters customized resiliency planning at the neighborhood level engaging citizen scientists as partners. It aligns with the National Science Foundation's mission to provide transparent and accessible information on risks and vulnerability, contributing to the development of smart and resilient communities nationwide.

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.

Barnali Dixon
Dr. Barnali Dixon is a Professor at School of Geosciences and contribute to Geography and Environmental Science and Policy programs. Dr. Barnali Dixon is the Director of the Geospatial Analytics Lab (G-SAL) and also the Executive Director and PI for the Initiative on Coastal Adaptation and Resilience (iCAR) at USF. Her research focuses on the development and application of Environmental Decision Support Systems (EDSS) integrated with Geospatial Technologies and geocomputation for modeling and managing land-water interfaces in the context of extreme weather events and climate change (including sea level rise) to facilitate informed data-driven planning, adaptation and resilience efforts with a particular emphasis equitable resilience. She is interested in making the ‘smart city framework’ smarter by using a holistic approach that facilitates intentional inclusivity of direct and indirect engagement of citizens to develop customized solutions. Her recently completed project funded by AT&T included the development of an integrated Community Resiliency Information System (CRIS). Her current NSF-funded project is called ‘Design and Development of a Near Real-Time Community Crowdsourced Resilience Information System for Enhancing Community Resilience in the Face of Flooding and other Extreme Events’. She is particularly interested in the development of transdisciplinary and spatially explicit models using Artificial Intelligence tools (AI) such as fuzzy logic, Artificial Neural Networks, Support Vector Machine, Relevance Vector Machine, Random Forest and other machine learning algorithms and soft computing techniques. She is considered one of the leaders in the subfield of geocomputation, where geospatial technologies intersect with machine learning and soft computing tools. She is the recipient of the Fulbright Specialist Award.
Performance Period: 10/01/2023 - 09/30/2026
Institution: University of South Florida
Award Number: 2325631