Intelligent Water Networks: Advanced Sensing, Real-Time Analytics, and Adaptive Management for Future Water Challenges
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: 20 December 2025 | Viewed by 101
Special Issue Editors
Interests: water resources; river engineering and management; sediment transport; computational fluid dynamics (CFD); artificial intelligence (AI); image analysis (QS4)
Interests: artificial intelligence; open channel hydraulics; pipe network analysis; hydrodynamic modeling; flood modelling; environmental impact assessment rivers; anfis; neuro-fuzzy; sediment dynamics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Water systems across the globe are under increasing pressure due to climate change, population growth, aging infrastructure, and increasing water quality concerns. Traditional water management strategies often fall short of addressing these complex and interconnected challenges. This Special Issue seeks to highlight the development and application of intelligent water networks that leverage cutting-edge sensing technologies, real-time analytics, and adaptive management frameworks to enhance the resilience and responsiveness of water systems.
We invite original research articles, comprehensive reviews, and case studies focusing on innovative solutions throughout the water cycle. Topics of interest include, but are not limited to, the following:
- Development and deployment of novel sensor for monitoring water quality and quantity;
- Applications of edge computing and Internet of Things (IoT) in water infrastructure;
- Real-time data processing, analytics, and visualization techniques;
- Machine learning and artifical intelligence for predictive modeling and decision-making;
- Digital twins technologies for simulation and optimization of water systems;
- Smart decision support tools and cyber–physical system integration;
- Adaptive management approaches to improve climate resilience and water security;
- Multi-scale and integrated monitoring networks.
This Special Issue aims to promote transformative technologies and methodologies that bridge the gap between data acquisition and actionable water resource management, supporting global sustainability efforts.
Dr. Sohail Iqbal
Dr. Manousos Valyrakis
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
- intelligent water systems
- advanced sensors
- real-time analytics
- machine learning
- digital twins
- edge computing
- adaptive management
- IoT
- water quality monitoring
- predictive modeling
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