14 May 2020
14 May 2020
A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling
- 1LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Austria
- 2Upstream Tech, Natel Energy Inc., Alameda, CA, USA
- 3Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
- 1LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Austria
- 2Upstream Tech, Natel Energy Inc., Alameda, CA, USA
- 3Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
Abstract. A deep learning rainfall-runoff model can take multiple meteorological forcing products as inputs and learn to combine them in spatially and temporally dynamic ways. This is demonstrated using Long Short Term Memory networks (LSTMs) trained over basins in the continental US using the CAMELS data set. Using multiple precipitation products (NLDAS, Maurer, DayMet) in a single LSTM significantly improved simulation accuracy relative to using only individual precipitation products. A sensitivity analysis showed that the LSTM learned to utilize different precipitation products in different ways in different basins and for simulating different parts of the hydrograph in individual basins.
Frederik Kratzert et al.
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RC1: 'Review of HESS-2020-221', Anonymous Referee #1, 22 Jun 2020
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AC1: 'Response to reviewer #1', Frederik Kratzert, 22 Jul 2020
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AC1: 'Response to reviewer #1', Frederik Kratzert, 22 Jul 2020
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RC2: 'Review of hess-2020-221', Anonymous Referee #2, 09 Jul 2020
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AC2: 'Reponse to reviewer #2', Frederik Kratzert, 22 Jul 2020
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AC2: 'Reponse to reviewer #2', Frederik Kratzert, 22 Jul 2020
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EC1: 'Final Editor's comment', Dimitri Solomatine, 07 Aug 2020
Frederik Kratzert et al.
Frederik Kratzert et al.
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