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Deep learning rainfall–runoff predictions of extreme events
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2022
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Source: Hydrol. Earth Syst. Sci., 26, 3377–3392
[PDF-1.57 MB]
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Journal Title:Hydrology and Earth System Sciences
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NOAA Program & Office:
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Description: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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Source:Hydrol. Earth Syst. Sci., 26, 3377–3392
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Rights Information:CC BY
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Compliance:Submitted
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