2026 ASEE Annual Conference & Exposition

HINT: An Educational Dataset of U.S. East Coast Hurricanes and Tide Gauge Data for Climate Resilience

Presented at DSAI-Session 12: Data Science Projects, Datasets, and Real-World Applications

Climate change and global warming have intensified the frequency and impact of hurricanes and coastal flooding, posing major challenges to communities along the U.S. East Coast. Understanding these risks requires integrated datasets that capture both meteorological and hydrological factors. However, existing data sources are fragmented, and many coastal tide gauge stations contain gaps or incomplete records.

In this work, we present a novel dataset entitled HINT, which merges hurricane historical records from the International Best Track Archive for Climate Stewardship (IBTrACS) with water level measurements from NOAA tide gauge stations. Each storm is augmented with measurements from all available tide gauges within its path or near landfall, producing multiple storm-station pairs when several gauges are matched. We decided to retain the storms without any valid station data due to distance from land, inactive or nonexistent stations, but we explicitly added a corresponding flag to indicate the absence of tide gauge augmentation. This approach preserves the full historical coverage of IBTrACS while enabling enriched analysis wherever observational data exist.

Beyond research applications, the dataset is designed as an educational resource for engineering and data science programs. It provides a platform for students to explore data cleaning, integration, and analysis tasks while studying the intersection of climate resilience, coastal engineering, and applied machine learning. Faculty can incorporate the dataset into project-based learning activities, allowing students to practice technical skills in a real-world, societally relevant context. This contribution bridges the gap between data science and climate change education, providing the ASEE community with an open, reproducible dataset that supports both research and teaching in climate resilience and engineering education.

Authors

Dr. Thabet Kacem University of the District of Columbia [biography]

Thabet Kacem is a tenured associate professor of computer science at the University of the District of Columbia. He obtained his PhD in information technology at George Mason University in May 2016. He obtained his master’s in computer science at the University of the District of Columbia in January 2010. His research interests include cyber security, intelligent transportation systems, smart cities, cyber-physical systems, blockchains, and climate change. He is the principal inventor of the US Patent number 11022696 entitled “ADS-BSec: A holistic framework to secure ADS-B”. He has served as principal and co-principal investigator in several sponsored projects from NSF and NSA. His research sits at the intersection of Artificial Intelligence, Cybersecurity, and Cyber-Physical Systems, with a focus on developing trustworthy, intelligent, and resilient technologies for safety-critical infrastructures. His work integrates machine learning, deep learning, secure system design, and data-driven modeling to address challenges in intelligent transportation, mobile systems, and emerging digital infrastructures. He is particularly interested in building robust and explainable AI systems, improving digital trust and security, and advancing AI driven analytics for complex, real-world environments. Through interdisciplinary collaborations, his research aims to create practical, high-impact solutions that enhance safety, reliability, and decision-making in modern cyber-physical and socio-technical systems.
Dr. Bryan James Higgs University of the District of Columbia [biography]

Dr. Bryan Higgs is a native of southwest Virginia. Currently, he is an Assistant Professor in the Department of Civil Engineering at the University of the District of Columbia. Prior to this position, he was an Intelligence Community Postdoctoral Researcher.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

« View session