Articles | Volume 17, issue 3
https://doi.org/10.5194/esd-17-795-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-17-795-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jun 2026
Research article |
| 19 Jun 2026
| 19 Jun 2026
Deep learning-based chlorophyll prediction: comparison with a dynamic model and applications to fish catch forecasting
Ji-Sook Park
Department of Climate, Environment, and Energy, Jeonbuk National University, Jeonju, 54896, South Korea
Jong-Yeon Park
CORRESPONDING AUTHOR
Department of Climate, Environment, and Energy, Jeonbuk National University, Jeonju, 54896, South Korea
Department of Earth and Environmental Sciences & Earth Environmental System Research Center, Jeonju, 54896, South Korea
Department of Environmental Management, Seoul National University, Seoul, 08826, South Korea
Jeong-Hwan Kim
Center for Climate and Carbon Cycle Research, Korea Institute of Science and Technology, Seoul, 02792, South Korea
Woo Jin Jeon
Department of Climate, Environment, and Energy, Jeonbuk National University, Jeonju, 54896, South Korea
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Yoo-Geun Ham, Seon-Ho Nam, and Jin-Soo Kim
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2023-26, https://doi.org/10.5194/esd-2023-26, 2023
Manuscript not accepted for further review
Short summary
Short summary
Fires are inflicting substantial ecological and socio-economic impacts on a global scale. For the real-time monitoring the risk of fire incidents at an early stage, we developed a fire intensity estimation method based on the well-monitored meteorological variables. We utilized feed-forward neural networks (FFNNs) which uses four meteorological variables to estimate a fire radiative power. The estimation accuracy of FFNNs revealed a marked increase compared to a previous method.
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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.
We developed a deep learning system to predict future ocean phytoplankton, the base of the...
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