The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Deep learning rainfall–runoff predictions of extreme events
Open accessAbstract
Abstract. The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that the predictive accuracy of data-driven models based on deep learning may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis using long short-term memory (LSTM) networks and an LSTM variant that is architecturally constrained to conserve mass. The LSTM network (and the mass-conserving LSTM variant) remained relatively accurate in predicting extreme (high-return-period) events compared with both a conceptual model (the Sacramento Model) and a process-based model (the US National Water Model), even when extreme events were not included in the training period. Adding mass balance constraints to the data-driven model (LSTM) reduced model skill during extreme events.
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Cites
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Self-citationDeep learning and process understanding for data-driven Earth system science
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Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
(2018) · doi:10.5194/hess-22-6005-2018 · Cited by 2,114
(2019) · doi:10.5194/hess-23-5089-2019 · Cited by 1,223
Deep learning to represent subgrid processes in climate models
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Hybrid forecasting: blending climate predictions with AI models
Slater, L. J., Arnal, L., Boucher, M.-A., Chang, A. Y.-Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., et al. (2023) · Hydrology and Earth System Sciences · doi:10.5194/hess-27-1865-2023 · Cited by 281
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Machine learning applications for weather and climate need greater focus on extremes
Watson, P. A. G. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac9d4e · Cited by 32
Li, P., Song, Y., Pan, M., Lawson, K., & Shen, C. (2025) · Hydrology and Earth System Sciences · doi:10.5194/hess-29-6829-2025 · Cited by 7
Metadata
- Authors
- Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., Qualls, L. M., Gupta, H. V., & Nearing, G. S.
- Venue
- Hydrology and Earth System Sciences
- Publisher
- Copernicus GmbH
- Published
- 2022-07-05
- Volume
- 26
- Issue
- 13
- Pages
- 3377-3392
- ISSN
- 1027-5606 (ISSN-L)
- 1607-7938
- OpenAlex
- W4283811973
- Cited by
- 396
- References
- 29
- FWCI
- 21.44
- Type
- journal article
- Language
- en
- Open access
- gold
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- Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., Qualls, L. M., Gupta, H. V., & Nearing, G. S. (2022). Deep learning rainfall–runoff predictions of extreme events. Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-26-3377-2022
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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