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Session Proposal: MultiSector Dynamics: Using AI to Model and Manage Complex Adaptive Human-Earth Systems (AGU25)

GC083
MultiSector Dynamics: Using AI to Model and Manage Complex Adaptive Human-Earth Systems

Session ID#: 249350

Session Description:
Recent advances in Artificial Intelligence (AI) are quickly changing the landscape of tools available to conceptualize, execute, and disseminate research. We posit that research efforts in multi-sector systems could benefit from these advances, and we are thus interested in identifying and quantifying opportunities and challenges associated with their implementation. In this session, we aim to address these gaps and focus, in particular, on how AI can assist us to improve the current methodological approaches for modeling and managing complex adaptive Human-Earth systems. We welcome contributions on how AI can (1) expand the breadth and depth of data available for model development, (2) help us understand model structures, and (3) support model diagnostics of coupled model chains. We also welcome contributions on AI techniques that expand the current tools available for control problems, thereby allowing us to operate and plan highly nonlinear and interconnected Human-Earth systems.
Index Terms:

1622 Earth system modeling [GLOBAL CHANGE]
1906 Computational models, algorithms [INFORMATICS]
1916 Data and information discovery [INFORMATICS]
1942 Machine learning [INFORMATICS]
Primary Convener:  Stefano Galelli, Cornell University, School of Civil and Environmental Engineering, Ithaca, NY, United States
Conveners:  David Gold, Utrecht University, Department of Physical Geography, Utrecht, Netherlands, Jillian Paige Sturtevant, Baylor University, Department of Environmental Science, Waco, United States and Srishti Vishwakarma, Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, United States
Student/Early Career Convener:  Jillian Paige Sturtevant, Baylor University, Department of Environmental Science, Waco, United States
 
Improving Adaptive Decisions by Learning Which Information Should Trigger Them (Invited) (1913503)
Marta Zaniolo, Duke University, Durham, NC, United States
 
WaCSim: A GenAI-Integrated Cyber-Physical Simulation Platform for Water Distribution Systems (1957433)
Barak Fishbain, Technion Israel Institute of Technology, Civil and Environmental Engineering, Haifa, Israel, Stav Cohen, Technion Israel Institute of Technology, Data and Decision Sciences, Haifa, Israel and Mashor Housh, University of Haifa, Haifa, Israel
See more of: Global Environmental Change
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