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

First publication using LSTMs as rainfall-runoff model.

Abstract

In this paper, we propose a novel data-driven approach for rainfall-runoff modelling, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network. 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.

Paper

Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005-6022, 2018.

Citation

@article{kratzert2018rainfall,
  title={Rainfall--runoff modelling using Long Short-Term Memory (LSTM) networks},
  author={Kratzert, Frederik and Klotz, Daniel and Brenner, Claire and Schulz, Karsten and Herrnegger, Mathew},
  journal={Hydrology and Earth System Sciences},
  volume={22},
  number={11},
  pages={6005--6022},
  year={2018},
  publisher={Copernicus GmbH}
}