A Vision for Machine Learning and Artificial Intelligence in Great Lakes Research and Management

Dani Jones, Jing Liu, Scott Steinschneider, Lauren Fry, Lacey Mason, Andrea VanderWoude, Jia Wang, William S. Currie, Silvia Newell, Nathan Fox, William James Pringle , Mantha S. Phanikumar, Yi Hong, Anders Kiledal, Lindsay Fitzpatrick, Joy Shin, Andrew Gronewold, Sage Osborne, David Wright, Dan Titze, Paul Roebber, Hazem U. Abdelhady, Alisa Young, Joeseph Smith, Shelby Brunner, Joseph Langan, Pengfei Xue

The Laurentian Great Lakes are a vital freshwater resource and a regionally significant natural system facing complex, persistent, and compounding challenges from climate change, nutrient loading, and invasive species. The increasing availability of observational data, coupled with advances in computational power and machine learning (ML) and artificial intelligence (AI) methods, presents an opportunity to address these challenges by improving data integration and enabling powerful data-driven models. This perspective article outlines a broad vision for applying AI in Great Lakes research and management. We review the current state of AI efforts across several key topic areas and propose a cross-disciplinary roadmap focused on advanced modeling, multi-modal data fusion, and operational forecasting. Realizing this vision will require sustained investment in open data infrastructure, shared computational resources, and inter-institutional collaboration. If successful, this roadmap will accelerate research progress, improve decision-support tools, and enhance the resilience and sustainability of the Great Lakes region’s interconnected ecological and economic foundations.