Articles | Volume 26, issue 22
https://doi.org/10.5194/hess-26-5793-2022
https://doi.org/10.5194/hess-26-5793-2022
Research article
 | 
17 Nov 2022
Research article |  | 17 Nov 2022

How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models?

Reyhaneh Hashemi, Pierre Brigode, Pierre-André Garambois, and Pierre Javelle

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Cited articles

Beck, C., Jentzen, A., and Kuckuck, B.: Full error analysis for the training of deep neural networks, Infin. Dimens. Anal. Qu., 25, 2150020, https://doi.org/10.1142/S021902572150020X, 2022. a
Bengio, Y.: Practical recommendations for gradient-based training of deep architectures, in: Neural networks: Tricks of the trade, edited by: Montavon, G., Orr, G. B., and Müller, K.-R., Springer, 437–478, https://doi.org/10.1007/978-3-642-35289-8_26, 2012. a, b, c, d, e
Bracken, L. J. and Croke, J.: The concept of hydrological connectivity and its contribution to understanding runoff-dominated geomorphic systems, Hydrol. Process., 21, 1749–1763, https://doi.org/10.1002/hyp.6313, 2007. a
Burnash, R. J. C., Ferral, R. L., and McGuire, R. A.: A generalized streamflow simulation system: Conceptual modeling for digital computers, Cooperatively developed by the Joint Federal-State River Forecast Center, United States Department of Commerce, National Weather Service, State of California, Department of Water Resources, https://books.google.fr/books?hl=en&lr=&id=aQJDAAAAIAAJ&oi=fnd&pg=PR2&dq=A+generalised+streamflow+simulation+system+conceptual+modelling+for+digital+computers.,+Tech.+rep.,+US+Department+of+Commerce+National+Weather+Service+and+State+of+California+Department+of+Water+Resources&ots=4tUeYd75bu&sig=9E64OzUeZxuyF4ULMgxbQyr9ktI&redir_esc=y#v=onepage&q&f=false) (last access: 16 November 2022), 1973. a
Chiverton, A., Hannaford, J., Holman, I., Corstanje, R., Prudhomme, C., Bloomfield, J., and Hess, T. M.: Which catchment characteristics control the temporal dependence structure of daily river flows?, Hydrol. Process., 29, 1353–1369, https://doi.org/10.1002/hyp.10252, 2015. a
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Short summary
Hydrologists have long dreamed of a tool that could adequately predict runoff in catchments. Data-driven long short-term memory (LSTM) models appear very promising to the hydrology community in this respect. Here, we have sought to benefit from traditional practices in hydrology to improve the effectiveness of LSTM models. We discovered that one LSTM parameter has a hydrologic interpretation and that there is a need to increase the data and to tune two parameters, thereby improving predictions.