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Advanced Aquaculture Water Quality Management Research
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Advanced Aquaculture Water Quality Management Research

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Quality and Contamination".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 1767

Special Issue Editor

Dr. Subha M. Roy

E-Mail Website
Guest Editor
Smart Aquaculture Research Centre, Chonnam National University, Yeosu 59626, Republic of Korea
Interests: water quality; recirculating aquaculture system; machine learning; aeration system; degasser

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a platform for cutting-edge research and innovative applications in the field of, e.g., sustainable aquaculture, water treatment, AI in aquaculture, etc. Rapid technological advancements, growing environmental concerns, and the demand for resource-efficient systems have highlighted the need for novel approaches, integrated strategies, and interdisciplinary collaboration. The Special Issue invites contributions that address fundamental principles, practical implementations, and emerging trends that advance both scientific understanding and real-world applications.

We welcome original research articles, reviews, case studies, and technical notes that cover (but are not limited to) the following topics:

  1. Aquatic water quality management;
  2. Recirculating aquaculture system design and optimization;
  3. Artificial intelligence, machine learning, and data-driven modeling;
  4. Advanced materials, devices, and technologies;
  5. Energy efficiency and sustainable practices;
  6. Challenges, limitations, and future research directions.

Dr. Subha M. Roy
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water treatment and reuse
  • recirculating water treatment
  • flow-through-systems
  • AI/ML in aquacuture water
  • computational modeling and simulation
  • advanced aeration and filtration technologies
  • circular bioeconomy in aquaculture water quality
  • eco-friendly aquaculture practices
  • advanced materials for aquaculture systems
  • energy efficiency in aquaculture

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

16 pages, 1756 KB  
Article
Evaluating Performance Limitations in Aquaponic vs. Hydroponic: Dynamics of Nutrient Release by Fish and Accumulation Rate in Plants
by Syed Ejaz Hussain Mehdi, Aparna Sharma, Suleman Shahzad, Woochang Kang, Sandesh Pandey, Byung-Jun Park, Hyuck-Soo Kim and Sang-Eun Oh
Water 2026, 18(6), 742; https://doi.org/10.3390/w18060742 - 22 Mar 2026
Viewed by 501
Abstract
Aquaponics (AP) is the combination of aquaculture and hydroponic systems, developed based on waste to wealth theory. This study compared the plant growth and overall productivity of an aquaponic system (AP) with a controlled hydroponic system (HP) to assess the AP system’s performance [...] Read more.
Aquaponics (AP) is the combination of aquaculture and hydroponic systems, developed based on waste to wealth theory. This study compared the plant growth and overall productivity of an aquaponic system (AP) with a controlled hydroponic system (HP) to assess the AP system’s performance and identification of the performance-limiting factors. This comparative study spanned over a 35-day period, supported by batch tests for the nutrient accumulation rate in plants and the NH4+-N excretion rate by fish as a baseline for the system design. HP performed better in terms of plant growth, showing a mean plant fresh weight (g) of 165.6 ± 3.01 while AP showed 147.0 ± 4.6. Nutrient accumulation was better in HP for K and P; however, Ca2+, Mg2+, and Fe accumulation was higher in AP plants. The AP system supported a better fish growth of 31.95 ± 3.21% (FCR 1.29 ± 0.1, SGR 0.79 ± 0.06, and PER 2.24 ± 0.18) and a moderate plant biomass production. Further system design modifications and integrations are required to optimize the nutrient availability and sustainability of the AP systems. Full article
(This article belongs to the Special Issue Advanced Aquaculture Water Quality Management Research)
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17 pages, 2833 KB  
Article
Variable Selection and Model Comparison for Optimizing Machine Learning-Based TOC Prediction
by Kang Bin Ju and Dong Woo Jang
Water 2025, 17(23), 3367; https://doi.org/10.3390/w17233367 - 25 Nov 2025
Viewed by 791
Abstract
This study developed a rapid and real-time model for predicting total organic carbon (TOC), which is an alternative to the conventional biochemical oxygen demand (BOD) and chemical oxygen demand (COD) indicators. The influence of input variable selection methods and machine learning hyperparameter tuning [...] Read more.
This study developed a rapid and real-time model for predicting total organic carbon (TOC), which is an alternative to the conventional biochemical oxygen demand (BOD) and chemical oxygen demand (COD) indicators. The influence of input variable selection methods and machine learning hyperparameter tuning on TOC prediction accuracy was compared using ten-year water quality monitoring data. The analysis showed that TOC exhibited strong correlations with COD, T-P, BOD, and ammonia nitrogen (NH3-N). Principal component analysis confirmed that the primary factors driving TOC variation were associated with organic matter and nutrient pollution. Prediction models were developed using a multilayer perceptron (MLP) and random forest (RF). On average, the MLP model outperformed the RF model by approximately 20%, and COD consistently appeared as a critical predictor in all top-ranked feature sets. Finally, grid search-based hyperparameter tuning of the MLP model with the optimal variable set (DO, COD, T-P, DTP, PO4-P) increased the coefficient of determination from 0.7496 to 0.7562. The findings demonstrate that precise exploration of variable combinations and stronger model regularization are essential for improving prediction performance in TOC modeling. This study provides a foundation for future development of predictive models that integrate external environmental factors such as nonpoint source pollution. Full article
(This article belongs to the Special Issue Advanced Aquaculture Water Quality Management Research)
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