Interpreting the internals of Long Short-Term Memory networks for rainfall-runoff forecasting

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Source arXiv

Machine Learning

Atmospheric and Oceanic Physics

Stats Machine Learning

Interpreting the internals of Long Short-Term Memory networks for rainfall-runoff forecasting

NeuralHydrology -- Interpreting LSTMs in Hydrology

We look at the application of long short-term memory networks for rainfall-runoff forecasting, one of the central tasks in the field of hydrology, in which the river discharge has to be predicted from meteorological observations.

On basis of two different catchments, one with snowinfluence and one without, we demonstrate how the trained model can be analyzedand interpreted.

In the process, we show that the network internally learns to represent patterns that are consistent with our qualitative understanding of the hydrological system.

Authors

Frederik Kratzert, Mathew Herrnegger, Daniel Klotz, Sepp Hochreiter, Günter Klambauer

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