Articles | Volume 21, issue 12
Nat. Hazards Earth Syst. Sci., 21, 3679–3691, 2021
https://doi.org/10.5194/nhess-21-3679-2021

Special issue: Recent advances in drought and water scarcity monitoring,...

Nat. Hazards Earth Syst. Sci., 21, 3679–3691, 2021
https://doi.org/10.5194/nhess-21-3679-2021

Research article 03 Dec 2021

Research article | 03 Dec 2021

Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations

Elizaveta Felsche and Ralf Ludwig

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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 nhess-2021-110', Anonymous Referee #1, 10 Jun 2021
    • AC1: 'Reply on RC1', Elizaveta Felsche, 09 Jul 2021
  • RC2: 'Comment on nhess-2021-110', Anonymous Referee #2, 17 Jun 2021
    • AC2: 'Reply on RC2', Elizaveta Felsche, 09 Jul 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (20 Jul 2021) by Athanasios Loukas
AR by Elizaveta Felsche on behalf of the Authors (16 Aug 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (17 Aug 2021) by Athanasios Loukas
RR by Anonymous Referee #3 (01 Sep 2021)
RR by Anonymous Referee #2 (17 Sep 2021)
ED: Publish subject to minor revisions (review by editor) (27 Sep 2021) by Athanasios Loukas
AR by Elizaveta Felsche on behalf of the Authors (25 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (26 Oct 2021) by Athanasios Loukas
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Short summary
This study applies artificial neural networks to predict drought occurrence in Munich and Lisbon, with a lead time of 1 month. An analysis of the variables that have the highest impact on the prediction is performed. The study shows that the North Atlantic Oscillation index and air pressure 1 month before the event have the highest importance for the prediction. Moreover, it shows that seasonality strongly influences the goodness of prediction for the Lisbon domain.
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