Innovative AI Project to Enhance Coastal Storm Response, Led by Rice University

Rice University engineers are spearheading a groundbreaking project to refine coastal storm response using advanced artificial intelligence. Their work, supported by a $1.5 million NSF grant, promises to provide emergency responders with real-time, equitable and reliable insights, boosting community resilience in the face of hazardous weather events.

Rice University is leading an interdisciplinary effort to fortify coastal communities against the compounded risks of severe weather, by harnessing the power of responsible artificial intelligence (AI). This initiative aims to enhance the safety and resilience of these vulnerable communities.

At the project’s helm is Jamie Padgett, Rice’s Stanley C. Moore Professor in Engineering and chair of the Department of Civil and Environmental Engineering. Collaborating with colleagues Ben Hu and Avantika Gori from Rice and David Retchless from Texas A&M University at Galveston, Padgett’s team is poised to provide emergency responders with timely and reliable insights during tropical cyclones and coastal storm events.

“Our goal with this project is to enable communities to better prepare for and navigate severe weather by providing better estimates of what is actually happening or might happen within the next hours or days,” said Padgett in a news release.

The team, leveraging expertise from Rice’s Severe Storm Prediction, Education and Evacuation from Disasters (SSPEED) Center and the Ken Kennedy Institute, alongside A&M-Galveston’s Institute for a Disaster Resilient Texas, is developing the OpenSafe.AI system. This intelligent framework aims to integrate hazard and resilience models with multimodal urban data, assisting in decision-making throughout storm cycles.

OpenSafe.AI will meticulously factor in multiple hazards, including high-speed winds, storm surges and compound flooding, to predict their potential impacts on crucial infrastructure. This can range from transportation disruptions to hazardous material spills triggered by severe weather.

“By combining cutting-edge AI with a deep understanding of the needs of emergency responders, we aim to provide accurate, real-time information that will enable better decision-making in the face of disasters,” Hu, associate professor of computer science at Rice, said in the news release.

The research effort is rooted in user-centered design, resilience modeling and the principles of responsible AI. Utilizing focus groups and interviews with emergency response organizations, the team identified a critical deficit in integrated, scientifically sound information and technology platforms for managing severe storm impacts in urban coastal regions.

“Our goal is not only to develop a powerful tool for emergency response agencies along the coast but to ensure that all communities ⎯ especially the ones most vulnerable to storm-induced damage ⎯ can rely on this technology to better respond to and recover from the devastating effects of coastal storms,” added Gori, assistant professor of civil and environmental engineering at Rice.

Retchless, associate professor of geography and marine and coastal environmental sciences at Texas A&M, highlighted the project’s broader implications.

“This project has the potential to revolutionize how we approach disaster resilience, particularly in areas that are most vulnerable to the impacts of climate change, by leveraging the best of hazard and infrastructure impact modeling and harnessing advances in AI,” he said.

In the pursuit of inclusive and effective solutions, the team will engage with stakeholders from Houston and Galveston, ensuring the technologies developed are grounded in real community needs.

“We are committed to a collaborative and inclusive approach that will help us ground our research in community needs,” added Padgett.

The long-term vision for OpenSafe.AI encompasses adaptability and scalability to support global regions facing similar climate challenges, ultimately striving for widespread, equitable resilience to climate-driven hazards.