2019 Spring Cyberseminar Series: Process-guided deep learning: Improving water resource predictions with advanced hybrid models

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 water quality variables) will be presented. 

CUAHSI's 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.

All talks take place on Fridays at 1:00 p.m. ET.

Dates, Speakers, and Topics:

  • March 29, 2019: Machine Learning & Information Theory for Land Model Benchmarking & Process Diagnostics | Grey Nearing, University of Alabama
  • April 5, 2019: Long Short-Term Memory (LSTM) networks for rainfall-runoff modeling | Frederik Kratzert, Johannes Kepler University
  • April 12, 2019: Use deep convolutional neural nets to learn patterns of mismatch between a land surface model and GRACE satellite | Alex Sun, University of Texas at Austin
  • April 19, 2019: Long-term projections of soil moisture using deep learning and SMAP data with aleatoric and epistemic uncertainty estimates | Chaopeng Shen, Pennsylvania State University
  • April 26, 2019: Exploring deep neural networks to retrieve rain and snow in high latitudes using multi-sensor and reanalysis data | Guoqiang Tang, Tsinghua University
  • May 3, 2019: Process-guided deep learning: Improving water resource predictions with advanced hybrid models | Jordan S Read, USGS and Vipin Kumar, University of Minnesota
  • May 10, 2019: Remote sensing precipitation using artificial neural networks and machine learning methods | Kuolin Hsu, University of California, Irvine

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