water-logo

Journal Browser

Journal Browser

Artificial Intelligence in Water Science: Opportunities, Prospects, and Concerns

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Wastewater Treatment and Reuse".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 1194

Special Issue Editor


E-Mail Website
Guest Editor
School of Environment, Nanjing University, Nanjing 210023, China
Interests: wastewater treatment; microbial community; artificial intelligence; machine learning; quorum sensing; emerging contaminant
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of artificial intelligence (AI) in water science is rapidly advancing research and practice. Machine learning, deep learning, and predictive modeling have led to significant advancements in wastewater treatment, pollution monitoring, and water quality optimization. Notable achievements include enhanced flood prediction, real-time water quality monitoring, and improved efficiency in wastewater treatment and irrigation management. AI’s ability to process large datasets and improve decision-making supports more effective and sustainable water management practices. These innovations are crucial for addressing global water challenges and ensuring long-term water sustainability.

We invite authors to submit original research and review articles that explore the application of artificial intelligence (AI) in conventional water science fields such as wastewater, groundwater, and surface water. Additionally, we encourage submissions that investigate the application of AI in conjunction with materials science, biology, and other related disciplines, to unlock innovative solutions to complex water-related challenges.

Topics include, but are not limited to, the following:

  • AI-driven construction of wastewater treatment processes;
  • AI-driven efficiency enhancement of wastewater treatment processes;
  • AI-driven smart water management;
  • AI-driven exploration of pollutant fate and detection technology innovation;
  • Predictive modeling for groundwater contamination and remediation;
  • AI applications in real-time water quality monitoring and management.

Dr. Jinfeng Wang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • artificial intelligence
  • machine learning
  • emerging contaminant
  • wastewater treatment
  • groundwater
  • surface water

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.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Review

18 pages, 854 KiB  
Review
Water Quality Management in the Age of AI: Applications, Challenges, and Prospects
by Shubin Zou, Hanyu Ju and Jingjie Zhang
Water 2025, 17(11), 1641; https://doi.org/10.3390/w17111641 - 28 May 2025
Viewed by 821
Abstract
Artificial intelligence (AI) is transforming water environment management, creating new opportunities for improved monitoring, prediction, and intelligent regulation of water quality. This review highlights the transformative impact of AI, particularly through hybrid modeling frameworks that integrate AI with technologies like the Internet of [...] Read more.
Artificial intelligence (AI) is transforming water environment management, creating new opportunities for improved monitoring, prediction, and intelligent regulation of water quality. This review highlights the transformative impact of AI, particularly through hybrid modeling frameworks that integrate AI with technologies like the Internet of Things (IoT), Remote Sensing (RS), and Unmanned Monitoring Platforms (UMP). These advances have significantly enhanced real-time monitoring accuracy, expanded the scope of data acquisition, and enabled comprehensive analysis through multisource data fusion. Coupling AI models with process-based models (PBM) has notably enhanced predictive capabilities for simulating water quality dynamics. Additionally, AI facilitates dynamic early-warning systems, precise pollutant source tracking, and data-driven decision-making. However, significant challenges remain, including data quality and accessibility, model interpretability, monitoring of hard-to-measure pollutants, and the lack of system integration and standardization. To address these bottlenecks, future research should focus on: (1) constructing high-quality, standardized open-access datasets; (2) developing explainable AI (XAI) models; (3) strengthening integration with digital twins and next-generation sensors; (4) improving the monitoring of trace and emerging pollutants; and (5) coupling AI with PBM by optimizing input data, internal mechanisms, and correcting model outputs through validation against observations. Overcoming these challenges will position AI as a central pillar in advancing smart water quality governance, safeguarding water security, and achieving sustainable development goals. Full article
Show Figures

Figure 1

Back to TopTop