Articles | Volume 29, issue 6
https://doi.org/10.5194/hess-29-1749-2025
https://doi.org/10.5194/hess-29-1749-2025
Technical note
 | 
26 Mar 2025
Technical note |  | 26 Mar 2025

Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell

Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret

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When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models
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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
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Unveiling the Limits of Deep Learning Models in Hydrological Extrapolation Tasks
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Cited articles

Acuna Espinoza, E.: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell, Zenodo [code and data set], https://doi.org/10.5281/zenodo.14780059, 2025. a, b
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. a
Chien, H.-Y. S., Turek, J. S., Beckage, N., Vo, V. A., Honey, C. J., and Willke, T. L.: Slower is better: revisiting the forgetting mechanism in LSTM for slower information decay, arXiv [preprint], 2105.05944, https://doi.org/10.48550/arXiv.2105.05944, 2021. a
Donoho, D.: 50 years of data science, J. Comput. Graph. Stat., 26, 745–766, https://doi.org/10.1080/10618600.2017.1384734, 2017. a
Gauch, M., Kratzert, F., Klotz, D., Nearing, G., Lin, J., and Hochreiter, S.: Data for “Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network”, Zenodo [data set], https://doi.org/10.5281/zenodo.4072701, 2020a. a
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Short summary
Long short-term memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modelling. However, most studies focus on predictions at a daily scale, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting. In this study, we introduce a new architecture, multi-frequency LSTM (MF-LSTM), designed to use inputs of various temporal frequencies to produce sub-daily (e.g. hourly) predictions at a moderate computational cost.
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