Chaopeng Shen describes various types of machine learning with applications linked to traditional process based modeling in hydrology.

Physics-informed Machine Learning for Discovering Knowledge in Hydrology

The Water Resources Podcast is designed to conduct interviews with experts to discuss challenges with water resources related to climate, such as droughts and floods, and overexploitation by humans. Potential solutions to these challenges are also discussed. Regions covered range from local to global scale, and approaches considered include satellite and ground-based monitoring, and global to regional modeling. The podcast is hosted by Bridget Scanlon, Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin.

The Water Resources Podcast is a production of the Bureau of Economic Geology, The Jackson School of Geosciences at the University of Texas at Austin in partnership with the National Academy of Engineering. The podcast is supported by the Fisher Foundation.

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The Water Resources Podcast is designed to conduct interviews with experts to discuss challenges with water resources related to climate, such as droughts and floods, and overexploitation by humans. Potential solutions to these challenges are also discussed. Regions covered range from local to global scale, and approaches considered include satellite and ground-based monitoring, and global to regional modeling. The podcast is hosted by Bridget Scanlon, Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin. The Water Resources Podcast is a production of the Bureau of Economic Geology, The Jackson School of Geosciences at the University of Texas at Austin in partnership with the National Academy of Engineering. The podcast is supported by the Fisher Foundation.

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Physics-informed Machine Learning for Discovering Knowledge in Hydrology

Physics-informed Machine Learning for Discovering Knowledge in Hydrology

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