The Hydrologic Uncertainty Technical committee’s mission includes three “big research questions”. The second is “How to reduce uncertainty in understanding, modelling, and predicting the future of coupled human-hydrologic systems?” We’ve put together a list of sessions that emphasizes this research problem. Machine learning provides a particular approach to this research problem – we’ve put together a list of sessions that emphasizes machine learning.

H093 – Machine Learning in Hydrologic Modeling

NG004 – Data Assimilation meets Machine Learning

IN033 – Incorporating physics and domain knowledge to improve interpretability, explainability, reliability and generalization of Machine Learning Models (MLM) in the geosciences

Domain-specific problems

H045 – Domain-Aware Machine Learning for Subsurface Applications

H138 – Utility of Artificial Intelligence/Machine Learning Approaches in Soil Hydrological Processes and Agriculture

H002 – Advances and Applications of Data Integration, Inverse Methods, and Machine Learning in Hydrogeophysics

S015 – Extracting Information from Geophysical Signals with Machine Learning

S017 – Geophysical inversion, inference and imaging in the age of machine learning

U009 – Data Analytics and Machine Learning Innovation for Climate and Earth Surface Processes

EP027 – Machine Learning Applications in Earth Surface Processes Research

In other fields

GC006 – AI and Machine Learning for Climate and Extreme Weather Prediction

A137 – Use of Machine Learning and Causal Discovery to Advance Knowledge in the Atmospheric Sciences – Methods, Limitations and Trade-offs

A091 – Machine Learning for Climate Modeling and Inference

GC076 – Solar Radiation Monitoring and Forecast from Satellite Observations and Models: Physical and Machine Learning Perspectives

NG007 – Machine Learning in Space Weather

SH015 – Machine Learning and Data Assimilation as Emerging Tools for Characterization and Forecasting of Solar Variability and Space Weather Events

P022 – Machine Learning for Planetary Science

OS019 – Innovation and Exploration with Machine Learning in Ocean and Atmospheric Sciences: Global and Regional Applications

AGU 2019 Fall Meeting Sessions: Machine Learning

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