Leveraging Artificial Intelligence and Machine Learning to Advance Chesapeake Bay Research and Management: A review of status, challenges, and opportunities

February 24, 2025 - February 25, 2025
Edgewater, MD

This workshop convened in-person on Monday-Tuesday, February 24-25, 2025 at the Smithsonian Environmental Research Center (SERC).

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The Scientific and Technical Advisory Committee (STAC) hosted a 2-day workshop in February 2025 for the purpose of bringing together federal, state, and academic partners to delve into the opportunities artificial intelligence (AI) and machine learning (ML) offer for analyzing large-scale environmental data, identifying research needs, and improving coordination within the Chesapeake Bay Program partnership. The aim of this collaborative workshop was to enhance data-driven approaches to support Chesapeake Bay restoration goals, ensuring more effective and informed management practices.

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Workshop Agenda: FINAL_STAC Leveraging AI Workshop Agenda

Workshop Materials:

• Literature Review Spreadsheet
• Workshop Participant List

Workshop Steering Committee: 
* STAC Member

• Qian Zhang, UMCES, Workshop Chair
• Matt Baker*, UMBC
• Isabella Bertani, UMCES
• Bill Dennison*, UMCES
• Lew Linker, EPA
• Kelly Maloney, USGS
• Robert Sabo, EPA
• Chaopeng Shen, PSU
• Gary Shenk, USGS
• Kim Van Meter,  PSU

Presentations

• Session I: Summarize recent AI/ML applications to the Chesapeake Bay ecosystem and lessons learned
  • Introductory Overview of AI and ML – Alison Appling (USGS)
  • Overview of Chesapeake Bay Restoration: CBP Goals & Outcomes – Gary Shenk (USGS)
  • Literature Summary of Watershed and Living Resources Studies Involving AI/ML – Kelly Maloney (USGS)
  • Literature Summary of Estuarine and Living Resources Studies Involving AI/ML – Jian Shen (VIMS)
  • AI/ML Integration of Satellite Remote Sensing: Data Harmonization Challenges and Gaps – Stephanie Schollaert Uz (NASA)
• Session II: Identify the challenges and gaps in applying AI/ML approaches to Chesapeake Bay data
  • GeoAI and Social Systems Modeling – Patrick Bitterman (Kent State University)
  • Integrated AI models to forecast land use change – Mike Evans (Chesapeake Conservancy)
  • Advances in water quality predictions: datasets and learning frameworks – Shuyu Chang (PSU)
  • Modeling Light Conditions in the York River Estuary by Anchoring Satellite Imagery with High-Frequency In-Situ Observations – David Parrish (VIMS)
  • Physical habitat is more than a sediment issue: A multi-dimensional habitat assessment indicates new approaches for river management – Matthew Cashman (USGS)
  • Observed and projected functional reorganization of riverine fish assemblages from global change – Taylor Woods (USGS)
  • Images to Info: the USGS Flow Photo Explorer – Jenn Fair (USGS)
  • Leveraging machine learning and expert knowledge to unravel the complexities of multiple freshwater ecosystem stressors – Sean Emmons (USGS)
• Session III: Develop recommendations and identify opportunities for harnessing the power of AI/ML approaches to address Chesapeake Bay issues
  • State-of-the-Art AI & Physics-Informed ML in Hydrology and Water Quality: Insights and synergies – Chaopeng Shen (PSU)

 

If you have any questions, please contact Meg Cole, STAC Coordinator, at colem@chesapeake.org.