Articles | Volume 16, issue 1
Model description paper
03 Jan 2023
Model description paper |  | 03 Jan 2023

Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake

Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2022-174', Juan Antonio Añel, 24 Aug 2022
    • AC1: 'Reply on CEC1', Shuqi Lin, 11 Oct 2022
  • RC1: 'Comment on gmd-2022-174', Anonymous Referee #1, 27 Aug 2022
    • AC2: 'Reply on RC1', Shuqi Lin, 11 Oct 2022
  • RC2: 'Comment on gmd-2022-174', Anonymous Referee #2, 06 Sep 2022
    • AC3: 'Reply on RC2', Shuqi Lin, 11 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Shuqi Lin on behalf of the Authors (11 Oct 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (12 Oct 2022) by Le Yu
RR by Anonymous Referee #1 (23 Oct 2022)
RR by Anonymous Referee #2 (25 Nov 2022)
ED: Publish subject to technical corrections (28 Nov 2022) by Le Yu
AR by Shuqi Lin on behalf of the Authors (05 Dec 2022)  Author's response    Manuscript
Short summary
The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.