Articles | Volume 29, issue 3
https://doi.org/10.5194/hess-29-785-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
13 Feb 2025
Research article |
| 13 Feb 2025
| 13 Feb 2025
A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Related authors
Hydrol. Earth Syst. Sci., 25, 2543–2566, 2021
Cited articles
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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
Anctil, F. and Lauzon, N.: Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions, Hydrol. Earth Syst. Sci., 8, 940–958, https://doi.org/10.5194/hess-8-940-2004, 2004. a, b
Arsenault, R., Brissette, F., Martel, J.-L., Troin, M., Lévesque, G., Davidson-Chaput, J., Gonzalez, M. C., Ameli, A., and Poulin, A.: A Comprehensive, Multisource Database for Hydrometeorological Modeling of 14,425 North American Watersheds, Scientific Data, 7, 243, https://doi.org/10.1038/s41597-020-00583-2, 2020 (data available at: https://osf.io/rpc3w/, last access: 1 May 2024). a, b
Arsenault, R., Martel, J.-L., Brunet, F., Brissette, F., and Mai, J.: Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models, Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, 2023a. a, b
