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.
Consider attending the 2019 CUAHSI Hydroinformatics Conference on Hydroinformatics for scientific knowledge, informed policy, and effective response!
July 29 - 31, 2019 at Brigham Young University in Provo, UT
The CUAHSI Conference on Hydroinformatics is uniquely focused on data science and technology for water resources and hydrology. This conference will include keynote speakers and oral, poster, and hands-on sessions. Start planning now to be a part of this important meeting.
We are pleased to announce the following Keynote Speakers:
- Ni-Bin Chang, University of Central Florida
- Tyler Erickson, Google Earth Engine and Google Earth Outreach
- Sara Larsen, Western States Water Council Water Data Exchange
- Manish Parashar, National Science Foundation
- Gene Shawcroft, Central Utah Water Conservancy District
- Chaopeng Shen, Pennsylvania State University
Register by June 15 (Early Bird) | July 15 (Regular).
A limited number of $750 travel grants are available to students, post-docs, and early career faculty affiliated with U.S. universities.
For more information, including how to register, click here.