Deep learning rainfall–runoff predictions of extreme events
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Published:2022-07-05
Issue:13
Volume:26
Page:3377-3392
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ISSN:1607-7938
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Container-title:Hydrology and Earth System Sciences
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language:en
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Short-container-title:Hydrol. Earth Syst. Sci.
Author:
Frame Jonathan M.ORCID, Kratzert FrederikORCID, Klotz Daniel, Gauch MartinORCID, Shalev Guy, Gilon Oren, Qualls Logan M., Gupta Hoshin V., Nearing Grey S.
Abstract
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.
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
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