{"id":902,"date":"2019-07-11T01:35:07","date_gmt":"2019-07-11T05:35:07","guid":{"rendered":"http:\/\/hydrouncertainty.org\/?p=902"},"modified":"2019-07-11T01:36:55","modified_gmt":"2019-07-11T05:36:55","slug":"agu-2019-fall-meeting-machine-learning","status":"publish","type":"post","link":"http:\/\/hydrouncertainty.org\/2019\/07\/11\/agu-2019-fall-meeting-machine-learning\/","title":{"rendered":"AGU 2019 Fall Meeting Sessions: Machine Learning"},"content":{"rendered":"\n
The Hydrologic Uncertainty Technical committee\u2019s mission<\/a> includes three \u201cbig research questions\u201d. The second is \u201cHow to reduce uncertainty in understanding, modelling, and predicting the future of coupled human-hydrologic systems?\u201d We\u2019ve put together a list of sessions that emphasizes this research problem. Machine learning provides a particular approach to this research problem – we\u2019ve put together a list of sessions that emphasizes machine learning.<\/p>\n\n\n\n H093 – Machine Learning in Hydrologic Modeling<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/75246<\/a><\/p>\n\n\n\n NG004 – Data Assimilation meets Machine Learning<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/77631<\/a><\/p>\n\n\n\n IN033 – Incorporating physics and domain knowledge to\nimprove interpretability, explainability, reliability and generalization of\nMachine Learning Models (MLM) in the geosciences<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/82358<\/a><\/p>\n\n\n\n H045 – Domain-Aware Machine Learning for Subsurface\nApplications<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/85474<\/a><\/p>\n\n\n\n H138 – Utility of Artificial Intelligence\/Machine Learning\nApproaches in Soil Hydrological Processes and Agriculture<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/74487<\/a><\/p>\n\n\n\n H002 – Advances and Applications of Data Integration,\nInverse Methods, and Machine Learning in Hydrogeophysics<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/71766<\/a><\/p>\n\n\n\n S015 – Extracting Information from Geophysical Signals with\nMachine Learning<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/80960<\/a><\/p>\n\n\n\n S017 – Geophysical inversion, inference and imaging in the\nage of machine learning<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/80845<\/a><\/p>\n\n\n\n U009 – Data Analytics and Machine Learning Innovation for\nClimate and Earth Surface Processes<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/83451<\/a><\/p>\n\n\n\n EP027 – Machine Learning Applications in Earth Surface\nProcesses Research<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/75302<\/a><\/p>\n\n\n\n GC006 – AI and Machine Learning for Climate and Extreme\nWeather Prediction<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/81937<\/a><\/p>\n\n\n\n A137 – Use of Machine Learning and Causal Discovery to\nAdvance Knowledge in the Atmospheric Sciences \u2013 Methods, Limitations and\nTrade-offs<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/76723<\/a><\/p>\n\n\n\n A091 – Machine Learning for Climate Modeling and Inference<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/78836<\/a><\/p>\n\n\n\n GC076 – Solar Radiation Monitoring and Forecast from\nSatellite Observations and Models: Physical and Machine Learning Perspectives<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/81059<\/a><\/p>\n\n\n\n NG007 – Machine Learning in Space Weather<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/72833<\/a><\/p>\n\n\n\n SH015 – Machine Learning and Data Assimilation as Emerging\nTools for Characterization and Forecasting of Solar Variability and Space\nWeather Events<\/p>\n\n\n\n https:\/\/agu.confex.com\/agu\/fm19\/prelim.cgi\/Session\/80120<\/a><\/p>\n\n\n\n P022 – Machine Learning for Planetary Science<\/p>\n\n\n\nDomain-specific problems<\/h2>\n\n\n\n
In other fields<\/h2>\n\n\n\n