Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
3.5
Journals
Water
Special Issues
Aquaculture Productivity and Environmental Sustainability, 2nd Edition
water-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Water Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Aquaculture Productivity and Environmental Sustainability, 2nd Edition

A Special Issue of Water (ISSN 2073-4441) belonging to the section "Water, Agriculture and Aquaculture".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 812

Editors

Dr. Changwei Li

E-Mail Website
Guest Editor
Agricultural Engineering Institute, Jiangsu University, Zhenjiang, China
Interests: recirculating aquaculture system; aquaculture wastewater treatment; moving bed biofilm reactor; biofloc; emerging pollutants
Special Issues, Collections and Topics in MDPI journals
Dr. Wenchang Liu

E-Mail Website
Guest Editor
School of Fisheries and Life Sciences, Shanghai Ocean University, Shanghai 201306, China
Interests: environmental engineering; aquaculture; wastewater treatment; resource recycling; sustainable aquaculture
Special Issues, Collections and Topics in MDPI journals
Dr. Gang Liu

E-Mail Website
Guest Editor
Ocean Academy, Zhejiang University, Zhoushan, 866 Yuhangtang Road, Hangzhou 310058, China
Interests: aquatic biological environmental engineering; sustainable aquaculture technolo-gies; high-throughput data processing and bioinformatics analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The demand for high-quality protein from the world's growing population is setting higher standards for aquaculture. However, rapid increases in aquaculture production have generated a wide range of severe environmental issues, most notably water pollution and land resource grabbing. Moreover, climate change, such as droughts, floods, global warming, and ocean acidification, will pose a further threat to global aquaculture production. For aquaculture growth to be sustainable, its environmental impact must be significantly reduced. To date, a number of adaptation strategies have been proven to improve aquaculture productivity and environmental sustainability.

This Special Issue welcomes both original research and reviews. Topics of interest include, but are not limited to, the following:

  • Integrated aquaculture;
  • Optimized aquacultural engineering;
  • Advances in aquaculture wastewater treatment;
  • Precise farming management;
  • Smart aquaculture.

Dr. Changwei Li
Dr. Wenchang Liu
Dr. Gang Liu
Guest Editors

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-anonymized 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

  • recirculating aquaculture systems
  • aquaculture engineering
  • wastewater treatment
  • environmental sustainability
  • precise farming management
  • smart aquaculture

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 6437 KB  
Article
Integrating Hydrochemistry and Explainable Machine Learning for Groundwater Quality Assessment in the Bismil Plain, Türkiye
by Sevgi Özgür Geter, Süreyya Betül Rufaioğlu, Ali Volkan Bilgili and Güzel Yılmaz
Water 2026, 18(15), 1902; https://doi.org/10.3390/w18151902 - 4 Aug 2026
Viewed by 618
Abstract
This study evaluates groundwater quality in the Bismil Plain (Diyarbakır, Southeast Türkiye) using a total of 208 samples collected from 26 wells during eight seasonal sampling periods conducted between 2022 and 2024. In each sample, pH, electrical conductivity (EC), and the major ions [...] Read more.
This study evaluates groundwater quality in the Bismil Plain (Diyarbakır, Southeast Türkiye) using a total of 208 samples collected from 26 wells during eight seasonal sampling periods conducted between 2022 and 2024. In each sample, pH, electrical conductivity (EC), and the major ions Ca2+, Mg2+, Na+, K+, Cl−, SO42−, HCO3− and NO3− were analyzed, and a WHO-based Water Quality Index (WQI) was calculated for every observation. The study combines classical hydrochemical interpretation methods, including descriptive statistics, hierarchical correlation analysis, variance inflation factor, and Piper and Gibbs diagrams, with an explainable machine learning framework integrating SHAP-based feature selection into Random Forest, XGBoost, support vector regression, and stacking ensemble models. In addition, spatial residuals were evaluated using Moran’s I and ordinary kriging, anomalies were identified using Isolation Forest and Local Outlier Factor algorithms, and predictive uncertainty was quantified through bootstrap resampling. WQI values ranged from 79.37 to 125.48 (mean: 99.67), with all samples classified only within the “Good” (49.5%) and “Poor” (50.5%) quality categories, indicating that the aquifer is close to a critical water-quality threshold. Spatially, the highest (poorest-quality) WQI values form a coherent zone in the south-western and central parts of the plain, whereas the central-eastern wells return the lowest values; the same pattern is reproduced by all four models. XGBoost and the stacking ensemble models showed comparable predictive performance (R2 = 0.911 and 0.910; RMSE = 3.29 and 3.27, respectively), while SHAP analysis identified EC as the dominant controlling factor, followed by NO3−, SO42−, Ca2+, Mg2+ and Cl− (mean |SHAP| = 6.86, 1.57, 1.10, 1.09, 0.85 and 0.72 WQI units, respectively). Moran’s I computed on the residual fields was −0.067 (p = 0.275) for XGBoost and −0.068 (p = 0.273) for the stacking ensemble, so ordinary kriging of these residuals produced an essentially null correction, whereas the SVR residuals remained spatially autocorrelated (I = 0.242; p = 0.001) and were meaningfully corrected by the geostatistical step. The originality of the study lies in integrating explainable machine learning, geostatistical residual analysis, anomaly detection, and bootstrap-based uncertainty assessment within a unified framework for a multi-season groundwater dataset, while also evaluating the effectiveness of spatial correction using a Moran’s I-based approach. Full article
(This article belongs to the Special Issue Aquaculture Productivity and Environmental Sustainability, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop