Articles | Volume 13, issue 9
Geosci. Model Dev., 13, 4253–4270, 2020
https://doi.org/10.5194/gmd-13-4253-2020
Geosci. Model Dev., 13, 4253–4270, 2020
https://doi.org/10.5194/gmd-13-4253-2020

Model evaluation paper 15 Sep 2020

Model evaluation paper | 15 Sep 2020

ML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrients

Benya Wang et al.

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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (21 Jul 2020)  Author's response    Manuscript
ED: Publish as is (23 Jul 2020) by Thomas Poulet
AR by Benya Wang on behalf of the Authors (26 Jul 2020)  Author's response    Manuscript
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Short summary
Surface water nutrients are essential to manage water quality, but it is hard to analyse trends. We developed a hybrid model and compared with other models for the prediction of six different nutrients. Our results showed that the hybrid model had significantly higher accuracy and lower prediction uncertainty for almost all nutrient species. The hybrid model provides a flexible method to combine data of varied resolution and quality and is accurate for the prediction of nutrient concentrations.