Articles | Volume 25, issue 9
Hydrol. Earth Syst. Sci., 25, 4947–4966, 2021
https://doi.org/10.5194/hess-25-4947-2021
Hydrol. Earth Syst. Sci., 25, 4947–4966, 2021
https://doi.org/10.5194/hess-25-4947-2021

Research article 09 Sep 2021

Research article | 09 Sep 2021

Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling

Kailong Li 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-2021-65', Anonymous Referee #1, 01 Apr 2021
    • AC1: 'Reply on RC1', kailong Li, 26 Apr 2021
  • RC2: 'Comment on hess-2021-65', Anonymous Referee #2, 09 Apr 2021
    • AC2: 'Reply on RC2', kailong Li, 26 Apr 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) (11 May 2021) by Stacey Archfield
AR by kailong Li on behalf of the Authors (22 Jun 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (29 Jun 2021) by Stacey Archfield
RR by Anonymous Referee #1 (16 Jul 2021)
RR by Anonymous Referee #2 (24 Jul 2021)
ED: Publish subject to minor revisions (review by editor) (26 Jul 2021) by Stacey Archfield
AR by kailong Li on behalf of the Authors (31 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (15 Aug 2021) by Stacey Archfield
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Short summary
We proposed a test statistic feature importance method to quantify the importance of predictor variables for random-forest-like models. The proposed method does not rely on any performance measures to evaluate variable rankings, which can thus result in unbiased variable rankings. The resulting variable rankings based on the proposed method could help random forest achieve its optimum predictive accuracy.