[ESIP-all] Abstracts due: 4 Aug - AGU Session: ML/AI and Process-based Models

[Natasha Stavros]

Planning to attend the American Geophysical Union (AGU) Fall Meeting?

This session might interest you or someone you know.

Abstracts Due: 4 August 2021 23:59 EDT/3:59 +1 GMT
Session ID: 123315
Session Title: IN031. Process-based modeling and AI/ML for Predicting Global Environmental Change
Section: Earth and Space Science Informatics
Virtual Only Session (selected by primary convener during session submission): No
View Session Details: https://agu.confex.com/agu/fm21/prelim.cgi/Session/123315

Session Summary:  We are experiencing catastrophic global environmental change: global warming, population growth, and the sixth mass extinction. There is a pressing need to understand why our world is changing and predict changes to adapt. Despite progress made through emerging data analytics technologies (e.g., artificial intelligence - AI and machine learning - ML), there remains a need to understand causal effects of interconnected human-natural systems. AI/ML alone cannot capture the feedbacks and processes connecting land, water, atmosphere, and biosphere. For this, we rely on process-based models (e.g., radiative transfer, hydrology, fire behavior) but they often prohibit operational uses. We invite presentations on novel applications of AI/ML and process-based models to provide near-real-time predictive capabilities. We encourage submissions to describe environmental processes and discuss the use of AI/ML for data collection, integration, inference, or computational efficiency. Methods can include: synthetic data, emulation, scale-aware, reduced order, imputation, and data fusion.


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E. Natasha Stavros<https://wkidsolutions.com/resume/>, PhD (She/Her)
Director of Earth Lab Analytics Hub<https://www.colorado.edu/earthlab/analytics-hub>
Cooperative Institute of Research for Environmental Science (CIRES)
University of Colorado Boulder


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