Articles | Volume 25, issue 5
Hydrol. Earth Syst. Sci., 25, 2951–2977, 2021
https://doi.org/10.5194/hess-25-2951-2021
Hydrol. Earth Syst. Sci., 25, 2951–2977, 2021
https://doi.org/10.5194/hess-25-2951-2021

Research article 31 May 2021

Research article | 31 May 2021

Machine-learning methods for stream water temperature prediction

Moritz Feigl et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2020-670', Salim Heddam, 30 Jan 2021
    • AC1: 'Authors answers to RC1', Moritz Feigl, 02 Mar 2021
  • RC2: 'Comment on hess-2020-670', Adrien Michel, 15 Feb 2021
    • AC2: 'Authors answers to RC2', Moritz Feigl, 04 Mar 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (17 Mar 2021) by Bettina Schaefli
AR by Moritz Feigl on behalf of the Authors (15 Apr 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (26 Apr 2021) by Bettina Schaefli
RR by Salim Heddam (27 Apr 2021)
ED: Publish as is (27 Apr 2021) by Bettina Schaefli
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Short summary
In this study we developed machine learning approaches for daily river water temperature prediction, using different data preprocessing methods, six model types, a range of different data inputs and 10 study catchments. By comparing to current state-of-the-art models, we could show a significant improvement of prediction performance of the tested approaches. Furthermore, we could gain insight into the relationships between model types, input data and predicted stream water temperature.