2019 Spring Cyberseminar Series: Machine Learning & Information Theory for Land Model Benchmarking & Process Diagnostics

Talk Description

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.

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: Multioutput neural networks for estimating flow-duration curves in ungaged catchments | Scott Worland, Cornell University and USGS
  • May 10, 2019: Remote sensing precipitation using artificial neural networks and machine learning methods | Kuolin Hsu, University of California, Irvine

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Registration is free! You must register for the series in order to attend. To register, click here.

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