Rainfall-Runoff modelling using Long Short-Term Memory (LSTM) networks

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.5194/hess-22-6005-2018. This is version 5 of this Preprint.


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Frederik Kratzert, Daniel Klotz, Claire Brenner, Karsten Schulz, Mathew Herrnegger


Rainfall-runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data driven models. In this paper, we propose a novel data driven approach, using the Long-Short-Term-Memory (LSTM) network, a special type of recurrent neural networks. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model, in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA + Snow-17, which underlines the potential of the LSTM for hydrological modelling applications.




Civil and Environmental Engineering, Earth Sciences, Engineering, Environmental Engineering, Hydrology, Physical Sciences and Mathematics


machine learning, Deep learning, hydrology, modelling, Rainfall-Runoff


Published: 2018-05-01 22:45

Last Updated: 2018-09-10 14:18

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CC BY Attribution 4.0 International

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