Process-guided deep learning: Improving water resource predictions with advanced hybrid models

2019 Spring Cyberseminar Series

Jordan Read / USGS
Vipin Kumar / University of Minnesota

Talk Description

Data growth and computational advances have created new opportunities to improve water resources modeling. Deep learning tools deliver improved prediction accuracy by resolving complex relationships in large quantities of data. Additionally, process knowledge has continued to grow, yielding finer resolution models that capture more complex interactions and can be applied at broader scales. Both modeling approaches have drawbacks that can impede scientific discovery, including data needs for DL models and the often rigid structures of process-based models.

We will discuss hybrid modeling approaches, called "process-guided deep learning", which have the potential to offset these limitations by integrating process understanding into advanced machine learning modeling techniques. We show how these hybrid modeling frameworks can better leverage the strengths of both model types. Examples of PGDL predictions (including water temperature and surface water extent dynamics) will be presented. 

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.