Deep learning rainfall-runoff predictions of extreme events

Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shelev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, Grey S. Nearing

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)3377-3392
Number of pages16
JournalHydrology and Earth System Sciences
Volume26
Issue number13
DOIs
StatePublished - Jul 5 2022

ASJC Scopus subject areas

  • Water Science and Technology
  • Earth and Planetary Sciences (miscellaneous)

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