Looking into the Future of Smart Water Management by Artificial Intelligence (AI)
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 2026 | Viewed by 1614
Special Issue Editor
Interests: risk; resilience; and reliability-based optimization for the design; assessment; and life-cycle management of engineered assets; artificial intelligence; critical infrastructures
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Rapid urbanization, climate change, aging infrastructure, and increasing water scarcity are placing unprecedented pressure on water systems worldwide. Traditional monitoring, prediction, and management approaches are no longer sufficient to ensure reliability, sustainability, and resilience. At the same time, advances in artificial intelligence, machine learning, deep learning, and data‑driven modeling are opening new pathways for transforming how water resources and water infrastructure are monitored, analyzed, and operated.
This Special Issue aims to explore emerging AI‑enabled methodologies that support smart, adaptive, and sustainable water management across the entire life cycle of water systems. We welcome contributions that introduce innovative computational frameworks, intelligent sensing technologies, predictive analytics, optimization strategies, and digital‑twin‑based solutions for improving the performance, safety, and resilience of water infrastructure. The goal is to highlight cutting‑edge research, identify open challenges, and shape the future of AI‑driven water management. Contributions are welcomed in (but not limited to) the following areas:
- AI‑based monitoring and forecasting of water demand, quality, and availability;
- Machine learning and deep learning for leak detection, anomaly detection, and pipeline condition assessment;
- Intelligent control and optimization of water distribution and wastewater treatment systems;
- Digital twins for real‑time water infrastructure management;
- Remote sensing and computer vision for water resource assessment;
- Data‑driven flood prediction, drought forecasting, and climate‑adaptive water planning;
- Reinforcement learning for autonomous water system operation;
- Surrogate modeling and simulation‑based risk assessment for water networks;
- Integration of IoT, sensors, and AI for smart water grids;
- Sustainability‑oriented and resilience‑driven design of water infrastructure.
Dr. Jafar Jafari-Asl
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
- smart water management
- artificial intelligence
- machine learning
- deep learning
- digital twins
- water infrastruc-ture resilience
- predictive analytics
- intelligent monitoring
- optimization methods
- climate‑adaptive water systems
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