Long Short-Term Memory (LSTM) networks for rainfall-runoff modeling

2019 Spring Cyberseminar Series

Frederik Kratzert / Johannes Kepler University

Talk Description

Over the last couple of years, deep learning methods have revolutionized many fields, such as computer vision and natural language processing. Recently, several studies have reviewed potential applications for such deep learning methods in the field of hydrology, or earth sciences in general. However, one critic often raised, albeit their non-questionable predictive power, is the black-box like nature of these models.

In this session we concentrate on a special (recurrent) neural network architecture that played an important role on the recent rise of deep learning methods, the Long Short-Term Memory network (LSTM). We look at both the predictive power, as well as ways to interpret the network internals, in the domain of rainfall-runoff modeling. For example we will see that a LSTM that was trained to predict sololy the discharge from time series of meteorological variables, learns by itself to model snow in its internal memory.

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.