Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.5194/hess-28-4187-2024 |
Lizenz  |
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| Titel (primär) | HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin |
| Autor | Kratzert, F.; Gauch, M.; Klotz, D.; Nearing, G. |
| Quelle | Hydrology and Earth System Sciences |
| Erscheinungsjahr | 2024 |
| Department | CER |
| Band/Volume | 28 |
| Heft | 17 |
| Seite von | 4187 |
| Seite bis | 4201 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Daten-/Softwarelinks | https://doi.org/10.4211/hs.474ecc37e7db45baa425cdb4fc1b61e1 https://doi.org/10.5281/zenodo.10139248 https://doi.org/10.5281/zenodo.13691802 |
| Abstract | Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets, typically data from only a single hydrological basin. In this position paper, we show that LSTM rainfall–runoff models are best when trained with data from a large number of basins. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29678 |
| Kratzert, F., Gauch, M., Klotz, D., Nearing, G. (2024): HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin Hydrol. Earth Syst. Sci. 28 (17), 4187 - 4201 10.5194/hess-28-4187-2024 |
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