Machine Learning & Information Theory for Land Model Benchmarking & Process Diagnostics
2019 Spring Cyberseminar Series
- Grey Nearing / University of Alabama
I would like to propose that there is significant room in the Hydrological Sciences for developing better methods for integrating machine learning and physical modeling.
This presentation will start by reviewing some recent results that compare machine learning and process-based Hydrology and Hydrometeorology models through benchmarking and process diagnostics. We will use information theory and dynamic process networks to look at the internal structure and functioning of complex systems models, and try to understand causes of missing information in process-based models.
The talk will conclude by outlining one particular strategy for combining machine learning with process modeling that involves adding a machine learning kernel to the numerical integration of a dynamical systems model. I’ll present results from applying this method to both rainfall-runoff modeling and soil moisture modeling.
2019 Spring Cyberseminar Series: Recent advances in big data machine learning in Hydrology
Hosted by Chaopeng Shen, Pennsylvania State University
Recently big data machine learning has led to substantial changes across many areas of study. In Hydrology, the introduction of big data and machine learning methods have substantially improved our ability to address existing challenges and encouraged novel perspectives and new applications. These advances present new opportunities methods that aid scientific discovery, data discovery, and predictive modeling. This series cover new techniques and findings that have emerged in Hydrology during the previous year, with a focus on catchment and land surface hydrology.