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Smart Design and Management of Water Distribution Systems
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Smart Design and Management of Water Distribution Systems

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 1410

Special Issue Editors

Dr. Xiao Zhou

E-Mail Website
Guest Editor
College of Civil Engineering, Hefei University of Technology, Hefei 230009, China
Interests: water distribution networks; intelligent algorithms; deep learning; graph neural network; graph signal processing; leakage detection; hydraulic models; burst detection; model calibration
Dr. Shipeng Chu

E-Mail Website
Guest Editor
College of Civil Engineering and Architecture, Zhejiang University, Zhejiang 310058, China
Interests: water distribution networks; online hydraulic models; system optimization analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proliferation of IoT sensors and SCADA systems has transformed Water Distribution Systems (WDSs) into data-rich cyber–physical environments. However, the transition from massive data to intelligent and actionable design and management solutions remains a significant challenge due to data irregularities, model uncertainties, and limited feature extraction capabilities. This Special Issue aims to explore the latest advancements in the smart design and management of WDS, with a particular focus on the synergy between multi-source data, hydraulic modeling, and advanced artificial intelligence.

We seek to bridge the gap between cutting-edge artificial intelligence—such as graph neural networks, physics-informed machine learning, and generative artificial intelligence—and practical engineering requirements. The goal is to facilitate the development of robust, scalable, and interpretable solutions for next-generation smart water infrastructure. We welcome original research that addresses theoretical innovations, methodological refinements, and real-world applications.

Recommended topics are as follows:

We invite submissions covering a broad range of topics, including but not limited to the following:

  • Application of advanced machine learning /deep learning methods in water distribution systems.
  • Intelligent leakage control and automated burst detection strategies.
  • Hydraulic state estimation and hydraulic model calibration.
  • Optimized design and rehabilitation of water infrastructure.
  • Smart scheduling and energy optimization for pumping stations.
  • Digital twins and real-time decision support systems for urban water management.
  • Resilience quantification and enhancement.
  • Water quality security and contaminant tracking in water distribution systems.
  • Sensor network optimization for security.

Dr. Xiao Zhou
Dr. Shipeng Chu
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 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

  • water distribution systems
  • deep learning
  • leakage detection
  • hydraulic models
  • model calibration
  • resilience
  • digital twin

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Published Papers (2 papers)

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Research

17 pages, 2539 KB  
Article
Large Language Models for Coagulant Dosage Prediction: A Systematic Investigation of Generalization, Rationale Patterns and Hallucination
by Xiujuan Li, Yisu Zhou, Chunhui Wang and Jingqing Liu
Water 2026, 18(10), 1132; https://doi.org/10.3390/w18101132 - 9 May 2026
Viewed by 552
Abstract
Precise coagulant dosage control is essential for stable drinking-water treatment, yet conventional machine learning (ML) methods can be sensitive to data conditions. This study evaluates a large language model (LLM)-based in-context workflow for this tabular prediction task using the DeepSeek family, benchmarked against [...] Read more.
Precise coagulant dosage control is essential for stable drinking-water treatment, yet conventional machine learning (ML) methods can be sensitive to data conditions. This study evaluates a large language model (LLM)-based in-context workflow for this tabular prediction task using the DeepSeek family, benchmarked against XGBoost, ANN, SVM, and k-NN under a shared chronological protocol. We examined performance across feature configurations, training-pool conditions, and outlier subsets. On the HQ-WTP case dataset, full-feature input outperformed temperature-only input, indicating the value of multivariate information. Performance responses to training-pool condition were model-dependent, with no universal optimum. Under the fixed protocol, in the full-feature test setting, the strongest tabular baseline showed the strongest test performance, while DeepSeek-Reasoner and DeepSeek-Chat showed intermediate performance, and DeepSeek-R1-Distill-Qwen-32B showed relatively lower stability. DeepSeek-Reasoner reached its best test performance at the 1/2 condition. We also coded narrative rationale patterns in generated responses and performed a protocol-based hallucination audit. DeepSeek-Reasoner showed comparatively lower hallucination incidence and more stable error behavior in this benchmark. These analyses are interpreted as response-level reliability evidence rather than verification of internal computational mechanisms. Overall, the study provides transparent, case-specific benchmark evidence for LLM-assisted decision support, while broader deployment claims require strict hallucination control, human-in-the-loop safeguards, and independent multi-site validation. Full article
(This article belongs to the Special Issue Smart Design and Management of Water Distribution Systems)
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21 pages, 4959 KB  
Article
Reservoir Inflow Risk-Window Early Warning Informed by Monitoring and Routing-Decay Modeling
by Boming Wang, Junfeng Mo, Ersong Wang, Zuolun Li and Yongwei Gong
Water 2026, 18(9), 1005; https://doi.org/10.3390/w18091005 - 23 Apr 2026
Viewed by 521
Abstract
Against the backdrop of multi-source water transfers and increasingly frequent extreme rainfall, short-term deterioration of reservoir inflow water quality has become a key risk to intake safety, treatment operations, and urban water-supply security. Traditional assessments based on static thresholds and annual or seasonal [...] Read more.
Against the backdrop of multi-source water transfers and increasingly frequent extreme rainfall, short-term deterioration of reservoir inflow water quality has become a key risk to intake safety, treatment operations, and urban water-supply security. Traditional assessments based on static thresholds and annual or seasonal averages often fail to identify high-risk periods at the event scale. Using continuous online monitoring data from 2021 to 2024 for the inflow of Yuqiao Reservoir, Tianjin, China, this study developed a month-specific dynamic-threshold framework and green/yellow/red risk windows and integrated a reach-wise river–reservoir routing scheme; a two-box decay model; and a three-class risk trigger into a unified analytical framework for long-term background characterization, event propagation analysis, source-contribution interpretation, and early-warning evaluation. Results show that the permanganate index (CODMn) exhibits an overall stable-to-declining background with pronounced wet-season pulses, whereas total nitrogen (TN) and total phosphorus (TP) remain at moderate-to-high levels, with yellow/red risk windows clustering markedly in the wet season. In typical red and yellow events, nitrogen contributions from upstream control sections progressively accumulate toward the reservoir inlet along the river–reservoir cascade system, whereas in some events the residual contribution from unmonitored near-inlet inflows becomes dominant. The CODMn-based three-class trigger achieves an overall accuracy of approximately 71.5% and shows comparatively strong identification of yellow-level risk, while remaining conservative for red-level alarms. These findings indicate that coupling month-specific dynamic thresholds with event-scale routing-decay analysis and trigger-based classification can support inflow monitoring, intake-risk early warning, and coordinated operation of key upstream reaches and near-reservoir control zones in water-transfer–reservoir integrated systems. Full article
(This article belongs to the Special Issue Smart Design and Management of Water Distribution Systems)
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