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Scalable predictive framework for environmental pathogen control in land-based aquaculture

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Abstract

Land-based aquaculture requires scalable treatment systems capable of anticipating and mitigating pathogenic risks under changing environmental conditions. In this study, we collected meteorological and bacterial data and performed correlation analyses to identify key relationships, which guided the development of an integrated, predictive treatment system. This system combines a modular total suspended solids–pathogen removal system (TSS–PRS), composed of sediment filtration, UV disinfection, and oxygen dissolution, with a deep learning-based multi-layer perceptron (MLP) model to improve water quality and forecast pathogen dynamics. The TSS–PRS effectively reduced TAN (41.1%), bacterial activity (BQV, 74.5%), and turbidity (72.8%). It also successfully eliminated hazardous fish pathogens, including Photobacterium damselae, Tenacibaculum maritimum, Vibrio harveyi, and Enteromyxum leei. The MLP model further indicated that bacterial activity markedly increased under optimal conditions of turbidity (100 NTU), pH (7.97), and water temperature (27.5 °C).

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Data availability

The environmental and microbial datasets generated and analyzed in this study are not publicly available due to institutional data-sharing agreements. However, they are available from the corresponding author upon reasonable request. Summary statistics, selected time-series data, and detailed descriptions of all key variables and data-processing steps are provided in the Supplementary Information.

Code availability

The code used for Spearman’s correlation analysis, MLP-based microbial risk prediction, and SEM is available from the corresponding author upon reasonable request. Detailed information on the model architecture, training parameters, and evaluation procedures is provided in the Supplementary Information.

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Acknowledgments

This research was supported by the Korean Institute of Marine Science & Technology Promotion (KIMST), funded by the Ministry of Oceans and Fisheries (RS-2022-KS221676). The authors thank Bong-Lae Kim and Ki-Ju Kim of Korea Aquaculture Engineering (KAE) for their technical assistance and support during field operations.

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Author notes

  1. These authors contributed equally: Hyunsoo Choi, Su-Mi Shin.

Authors and Affiliations

  1. Smart Aquaculture Research Center, Chonnam National University, Yeosu, 59626, Republic of Korea

    Hyunsoo Choi, Sungju Jung, Sung-Hun Lee & Taeho Kim

  2. Department of Aqualife Medicine, Chonnam National University, Yeosu, 59629, Republic of Korea

    Su-Mi Shin & Sungju Jung

  3. Department of Fishery, Marine, Industry, Tourism and Leisure, Chonnam National University, Yeosu, 59629, Republic of Korea

    Sung-Hun Lee

  4. Department of Marine Production Management, Chonnam National University, Yeosu, 59626, Republic of Korea

    Taeho Kim

Authors

  1. Hyunsoo Choi
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  2. Su-Mi Shin
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  3. Sungju Jung
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  4. Sung-Hun Lee
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  5. Taeho Kim
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Contributions

H.C. and S.M.S. conceptualized the study and designed the methodology. H.C., S.M.S., S.J., and S.H.L. conducted the investigation. H.C. and S.M.S. curated and analyzed the data and prepared the original manuscript draft. S.J. and T.K. reviewed and edited the paper. S.J. and T.K. supervised the project. T.K. acquired the funding. All authors reviewed and approved the final paper.

Corresponding author

Correspondence to
Taeho Kim.

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Choi, H., Shin, SM., Jung, S. et al. Scalable predictive framework for environmental pathogen control in land-based aquaculture.
npj Clean Water (2026). https://doi.org/10.1038/s41545-025-00550-7

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