Frederik Kratzert, Martin Gauch, Grey Nearing, Sepp Hochreiter, and Daniel Klotz

In recent years, methods of artificial intelligence have become essential components of almost all branches of science and technology. This also holds for the area of hydrology: multilayered neural networks—also known as Deep Learning models—allow for rainfall-runoff predictions at unprecedented accuracy. This contribution highlights the potential of Deep Learning for applications of water management. The first part of this article shows how so called Long Short-Term Memory networks— a Deep Learning architecture explicitly designed for time series prediction—can be used for rainfall-runoff modeling, and how this approach yields better results than any known hydrologic model on a number of hydrologic problems. The second part of this article outlines important properties of Long Short-Term Memory networks. We show that these models can process any available data. This allows them to draw synergetic information from multiple meteorological datasets, which improves predictions. Further, we demonstrate how these models learn relevant hydrologic processes (e.g., accumulation and melting of snow) without specifically being trained to do so.

Österreichische Wasser-und Abfallwirtschaft, 2021-05-17.

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IARAI Authors
Dr Sepp Hochreiter
Weather and Physics
Deep Learning, Hydrology, LSTMs, Rainfall-Runoff


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