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Deep learning rainfall–runoff predictions of extreme events

التفاصيل البيبلوغرافية
العنوان: Deep learning rainfall–runoff predictions of extreme events
المؤلفون: J. M. Frame, F. Kratzert, D. Klotz, M. Gauch, G. Shelev, O. Gilon, L. M. Qualls, H. V. Gupta, G. S. Nearing
المصدر: Hydrology and Earth System Sciences, Vol 26, Pp 3377-3392 (2022)
بيانات النشر: Copernicus Publications, 2022.
سنة النشر: 2022
المجموعة: LCC:Technology
LCC:Environmental technology. Sanitary engineering
LCC:Geography. Anthropology. Recreation
LCC:Environmental sciences
مصطلحات موضوعية: Technology, Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1027-5606
1607-7938
Relation: https://hess.copernicus.org/articles/26/3377/2022/hess-26-3377-2022.pdf; https://doaj.org/toc/1027-5606; https://doaj.org/toc/1607-7938
DOI: 10.5194/hess-26-3377-2022
URL الوصول: https://doaj.org/article/1ef8b1f9cdd0413c9f0aea6a8ad269f1
رقم الأكسشن: edsdoj.1ef8b1f9cdd0413c9f0aea6a8ad269f1
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:10275606
16077938
DOI:10.5194/hess-26-3377-2022