Articles | Volume 28, issue 5
https://doi.org/10.5194/hess-28-1191-2024
© Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
13 Mar 2024
Research article |
| 13 Mar 2024
| 13 Mar 2024
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia
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Hydrol. Earth Syst. Sci., 29, 2003–2021, 2025
Cited articles
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Australian Water Outlook: https://awo.bom.gov.au/, last access: February 2022.
Bennett, J. C., Wang, Q. J., Robertson, D. E., Schepen, A., Li, M., and Michael, K.: Assessment of an ensemble seasonal streamflow forecasting system for Australia, Hydrol. Earth Syst. Sci., 21, 6007–6030, https://doi.org/10.5194/hess-21-6007-2017, 2017.
Choi, J., Lee, J., and Kim, S.: Utilization of the Long Short-Term Memory network for predicting streamflow in ungauged basins in Korea, Ecol. Eng., 182, 106699, https://doi.org/10.1016/j.ecoleng.2022.106699, 2022.
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J., Tang, G., Gharari, S., Freer, J. E., Whitfield, P. H., and Shook, K. R.: The abuse of popular performance metrics in hydrologic modeling, Water Resour. Res., 57, e2020WR029001, https://doi.org/10.1029/2020WR029001, 2021.
