Session Leads: Kimberly Van Meter (Penn State), Ryan Woodland (Chesapeake Biological Laboratory/UMCES), and Jon Derek Loftis (VIMS)

Co-Lead(s): Shuyu Chang, Russell Lotspeich, Vyacheslav Lyubchich, Ryan Langendord, Jeremy Testa

Session Format: Oral Presentations

Session Description: 

The integration of machine learning (ML) approaches with ecosystem modeling holds significant promise for enhancing our understanding of complex environmental systems such as the Chesapeake Bay. This session aims to explore the intersection of cutting-edge technologies and Chesapeake Bay modeling to address pressing environmental challenges. Key discussion points will encompass strategies for data integration, showcasing the practical applications of ML algorithms in water quality modeling and habitat assessments, and methodologies for model calibration, validation, uncertainty, and sensitivity analysis. Moreover, the session will delve into stakeholder engagement strategies to communicate the value of data-driven modeling in informing management and policy decisions. Presentations in this session will highlight recent advancements in remote sensing techniques and artificial intelligence-augmented approaches to estuarine modeling and monitoring, leveraging passive and active sensors as inputs for different types of neural networks. Finally, this session will delve into the critical importance of understanding and predicting the effects of disturbance on coastal biota, emphasizing the development of sophisticated numerical models to predict dissolved oxygen concentrations and habitat availability. Presentations will explore state-of-the-art modeling approaches, spanning numerical, hybrid statistical-numerical, and network-based methods, with a focus on presenting emerging research into environmental interactions and scenario testing in Chesapeake Bay through data-driven innovation to address critical ecosystem management challenges.

Presentations (abstracts):

  1. Kim Van Meter, Shuyu Chang: Leveraging Machine Learning for Predictive Modeling of River Temperatures across the Chesapeake Bay Watershed: Assessing the Impacts of Changing Land Cover in a Changing Climate
  2. Jon Derek Loftis: Enhanced River Stage Detection Using a Deep Learning Algorithm Combining AI and Edge Detection
  3. John Hammond, Jeremy Diaz, Phillip Goodling, Aaron Heldmyer, Roy Sando: Forecasting the ecological impacts of hydrological drought in the Chesapeake Bay Watershed: Strategies for linking forecasted streamflow and groundwater conditions with potential biological and ecological responses
  4. Jian Shen: Machine Learning-based Wave Model with High Spatial Resolution in Chesapeake Bay
  5. Andrew Muller, Diana Lynn Muller: Creating a Long-Term Climatologically Based Forecast for Hypoxia in the Chesapeake Bay
  6. Carl Friedrichs, Dave Parrish, Chris Patrick, Willy Reay: Exploring Relationships Among and Controls on Estuarine Water Quality Parameters Using Unsupervised Clustering and Structural Equation Modeling
  7. Allison Dreiss, Jeremy Testa, Vyacheslav Lyubchich, Ryan Woodland, Ryan Langendorf: Modeling Impacts of Nutrient Reduction, and Warming on Benthic Forage and Hypoxia in the Chesapeake Bay
  8. Olivia N. Szot, Marjorie A.M. Friedrichs, Pierre St-Laurent, Aaron J. Bever, Courtney K. Harris: Mechanisms impacting variability of hypoxia onset in the Chesapeake Bay
  9. Vyacheslav Lyubchich, Ryan J. Woodland, Allison Dreiss, Ryan E. Langendorf, Jeremy M. Testa, Ryan Woodland: Predictability network of oxygen concentrations in Chesapeake Bay
  10. Ryan Woodland, Vyacheslav Lyubchich, Jeremy Testa, Allison Dreiss: Using machine learning to develop models of habitat suitability for a range of benthic taxa in Chesapeake Bay
  11. Kim Van Meter, Victor Schultz, Shuyu Chang: Quantifying Groundwater Nitrate Storage in the Upper Mississippi River Basin: Implications for Chesapeake Bay Watershed management
  12. Alexander H. Kiser, Benjamin Gressler, Lindsey Boyle, Sean Emmons, Taylor Woods, John Young, and Kelly Maloney: Updating the Biological Assessments of Non-Tidal Streams in the Chesapeake Bay Watershed: Improvements, Challenges, and Lessons Learned
  13. Shuyu Y Chang, Doaa Aboelyazeed, Kamlesh Sawadekar, Digant Chavda, Chaopeng Shen, Kimberly J Van Meter: Dams, nutrients, and water quality in the Chesapeake Bay Watershed
  14. Larry Davis: Accessible Smart GI Health Monitoring