Scholarlink

<p>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 <i>Photobacterium damselae</i>, <i>Tenacibaculum maritimum</i>, <i>Vibrio harveyi</i>, and <i>Enteromyxum leei</i>. 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).</p><p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

查看全文
  • Hyunsoo Choi,
  • Su-Mi Shin,
  • Sungju Jung,
  • Sung-Hun Lee,
  • Taeho Kim

摘要

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).