Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 9
Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4253-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4253-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 9
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, Matthew R. Hipsey, and Carolyn Oldham

Viewed

Total article views: 2,159 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,357 742 60 2,159 105 61 74
  • HTML: 1,357
  • PDF: 742
  • XML: 60
  • Total: 2,159
  • Supplement: 105
  • BibTeX: 61
  • EndNote: 74
Views and downloads (calculated since 06 Apr 2020)
Cumulative views and downloads (calculated since 06 Apr 2020)

Viewed (geographical distribution)

Total article views: 2,159 (including HTML, PDF, and XML) Thereof 1,925 with geography defined and 234 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Dec 2024
Download
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.
Read more