Articles | Volume 17, issue 3
https://doi.org/10.5194/esd-17-795-2026
https://doi.org/10.5194/esd-17-795-2026
Research article
 | 
19 Jun 2026
Research article |  | 19 Jun 2026

Deep learning-based chlorophyll prediction: comparison with a dynamic model and applications to fish catch forecasting

Ji-Sook Park, Jong-Yeon Park, Yoo-Geun Ham, Jeong-Hwan Kim, and Woo Jin Jeon

Viewed

Total article views: 3,768 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,049 1,535 184 3,768 93 91
  • HTML: 2,049
  • PDF: 1,535
  • XML: 184
  • Total: 3,768
  • BibTeX: 93
  • EndNote: 91
Views and downloads (calculated since 27 Nov 2025)
Cumulative views and downloads (calculated since 27 Nov 2025)

Viewed (geographical distribution)

Total article views: 3,768 (including HTML, PDF, and XML) Thereof 3,756 with geography defined and 12 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 19 Jun 2026
Download
Short summary
We developed a deep learning system to predict future ocean phytoplankton, the base of the marine food web. Using long-term records from climate model simulations and past ocean data, it provides skillful chlorophyll forecasts across global coastal regions, comparable to those from dynamic climate model forecasts. The predicted chlorophyll also explains historical changes in fish catch, offering a new tool to help communities prepare for climate-driven marine ecosystem changes.
Share
Altmetrics
Final-revised paper
Preprint