Long Short-Term Memory (LSTM) networks for rainfall-runoff modeling
2019 Spring Cyberseminar Series
- Frederik Kratzert / Johannes Kepler University
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.