Potential of Artificial Intelligence in Addressing Critical Challenges Related to Water Resources
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 390
Special Issue Editors
Interests: environmental engineering; combined processes; biological treatment; advanced (electrochemical) oxidation processes
Special Issues, Collections and Topics in MDPI journals
Interests: advanced process control; process fault detection and diagnosis; neural networks and neuro-fuzzy systems; multivariate statistical process control; optimal control of batch processes
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Our objective with this Special Issue is to emphasize the transformative potential of AI in addressing critical challenges related to environmental preservation, especially water resources.
This Special Issue aims to disseminate and discuss advances in AI technologies, promoting sophisticated solutions to enhance and ensure the quality of water treatment. We invite original and unpublished contributions in various application areas, including the following:
- Artificial intelligence for water quality surveillance and management.
- AI applications for the early detection of water pollution.
- Optimization of water treatment processes through artificial intelligence.
- Intelligent systems for sustainable water resource management.
We welcome contributions of unpublished research exploring various aspects of artificial intelligence for water treatment, such as the following:
- Modeling water treatment processes, including adsorption, photodegradation, oxidation, etc.
- Intelligent technologies for the real-time monitoring of water parameters.
- AI-based predictive modeling for water quality.
- Deep learning applications in the detection and elimination of contaminants.
- AI systems for optimizing water treatment processes.
- AI approaches for intelligent management of water networks.
This Special Issue extends an invitation to researchers, data scientists, environmental experts, and policymakers to contribute their original research, reviews, case studies, and insightful perspectives showcasing the novel applications and potential of AI and computation in the field of environmental management. We encourage a diversity of contributions to provide a comprehensive and in-depth view of these crucial advancements.
Prof. Dr. Abdeltif Amrane
Dr. Jie Zhang
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 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
- water treatment
- water management
- contaminants
- artificial intelligence
- deep learning applications
- predictive modeling
