Smart Simulation and Monitoring of Water Distribution Networks
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".
Deadline for manuscript submissions: 20 August 2026 | Viewed by 234
Special Issue Editors
Interests: water distribution systems; optimization; evolutionary algorithms; complex networks; machine learning
Interests: monitoring and biomonitoring water bodies; water, wastewater and sludge treatment; liquid waste treatment by oxidation technologies; disinfection of water and wastewater; reuse of water and sludge; energy from co-digestion
Special Issue Information
Dear Colleagues,
Water supply and distribution systems are one of the largest assets of industrial society. Yet, in many countries around the world, there is increasing evidence of inadequate water distribution networks, due to the deterioration of aging infrastructure, the rapid growth of urbanization, and the statutory and contractual quality standards that have to be offered to consumers.
Climate change impacts, together with the improvement in the quality of life and the process of migration towards urban areas, are conflicting drivers heading towards future scenarios of water resource scarcity under increasing consumption, requiring the need for a change in the paradigm of planning and management of such critical infrastructures.
In particular, the management of a water distribution system involves a continuous decision-making process, often under a high degree of uncertainty due, on the one hand, to the probabilistic character of time-dependent user demand and, on the other, to the lack of detailed information of the network itself. Water utilities are thus facing an ever-growing challenging context in which new strategic approaches are mandatory.
Recent technological advances, especially in the field of Information and Communication Technology, allow the implementation of operational methodologies supporting decision-makers in achieving good performance under different loading conditions. In particular, the integration among numerical modeling, real-time field data and remote-controlled devices are allowing greater flexibility.
Moreover, the disruptive advent of artificial intelligence and the new frontiers arising from technological advances in monitoring and computing shall thoroughly change the way in which smart water distribution systems will be operated.
The Special Issue will collect and compile papers showing several new approaches which contribute to the new paradigm of smart simulation and monitoring of water distribution networks, with the aim of providing new support to the scientific community, water utilities and relevant stakeholders, thereby strengthening the management and operational control of such critical infrastructures.
Topics covered:
- Data-driven simulations;
- Predictive ability of machine learning approaches;
- High-frequency pressure and acoustic sensors;
- Optimal placement of quality sensors for contamination prevention;
- Variable-speed pumps and valves for real-time pressure management and leakage control;
- Deep learning leak detection with advanced metering devices;
- Large language models for smart modeling and decision making;
- Smart meters data for modeling and forecasting water demand;
- Modeling free residual chlorine decay with smart water meter data.
Dr. Matteo Nicolini
Prof. Dr. Daniele Goi
Guest Editors
Manuscript Submission Information
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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
- data driven modelling
- real-time simulation
- smart sensors
- machine learning
- genetic algorithms
- artificial intelligence
- deep learning
- large language models
- leakage control
- predictive ability
- IoT
- quality
- disinfection by-products
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