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Advances in Management and Optimization of Urban Water Networks
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Advances in Management and Optimization of Urban Water Networks

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 7606

Special Issue Editors

Dr. Mohsen Hajibabaei

E-Mail Website
Guest Editor
Unit of Environmental Engineering, Department of Infrastructure, University of Innsbruck, 6020 Innsbruck, Austria
Interests: urban water management; complex network analysis; graph theory; sustainability; resilience analysis; environmental impact assessment
Prof. Dr. Robert Sitzenfrei

E-Mail Website
Guest Editor
Unit of Environmental Engineering, Department of Infrastructure Engineering, University of Innsbruck, 6020 Innsbruck, Austria
Interests: resilience; modelling of urban water networks; complex network analysis; transition modelling; smart water city
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mohsen Shahandashti

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
Interests: resilient infrastructure systems; large-scale infrastructure networks; asset management; risk-based performance modelin; optimization
Dr. Milad Latifi

E-Mail Website
Guest Editor
Centre for Water Systems, University of Exeter, Exeter, UK
Interests: water distribution networks; reliability; asset management; failure prediction; pressure management; water quality modelling; optimisation in water systems

Special Issue Information

Dear Colleagues,

The effective management and optimization of urban water networks (UWNs) are crucial for addressing the challenges posed by aging infrastructure, population growth, and climate change. Given the complex nature of UWNs, tackling these issues requires advanced analytical or data-driven techniques, innovative technologies, and strategic planning.

We are excited to introduce this Special Issue, titled "Advances in Management and Optimization of Urban Water Networks", which aims to explore advancements and practical solutions in the field of water distribution and drainage networks (including wastewater and stormwater). Our objective is to address the complex and diverse challenges associated with the management, optimization, and sustainability of UWNs by incorporating innovative technologies, optimization strategies, and sustainability practices.

This Special Issue aims to present a collection of papers addressing a wide range of research gaps in UWNs, such as the following:

Advanced modelling

Computationally efficient techniques and tools for UWN modelling.
Applications of artificial intelligence and machine learning in UWN management.
Applications of digital twins in informed decision making regarding UWN management.

Optimization algorithms and techniques

Optimization algorithms for common problems in UWNs, e.g., network design, sensor placement, leak detection, calibration, etc.
Role of emerging optimization algorithms in solving complex UWN problems.
Approaches for reducing the computational workload of optimization algorithms.

Digital water networks

Integration of IoT and smart technologies in UWNs.
The role of big data analytics in real-time water management.
Cybersecurity challenges and solutions for smart water systems.

Resilience assessment and enhancement

Strategies for assessing and enhancing resilience against natural and human-made disasters (e.g., earthquakes, floods, and cyber-attacks).
Asset investment to improve reliability, resiliency, and sustainability while mitigating vulnerability.
Trade-offs between different intervention strategies, as increased resilience to one failure mode may decrease resilience to another.
Interdependencies between UWNs and other infrastructure systems (e.g., road networks).

Sustainable management and operation

Management and operation of networks with a minimal environmental impact.
Green infrastructure integration.
Life cycle assessment (LCA) for the environmental impact analysis of UWNs.
Circular economy in water asset management.
Reactive, preventive, and predictive asset management in UWNs.

Innovative technologies

Emerging technologies (e.g., gen-AI, new sensors, drones) and their potential to improve the management of UWNs.
Blue sky ideas for the future management of UWNs.

Energy efficiency

Methods and technologies for improving energy efficiency in UWNs.
Water-energy nexus in urban water systems.
Energy harvesting from water mains and distribution networks.

Leakage and water loss

Leak detection in UWNs.
Estimating and reducing non-revenue water (NRW) in water distribution networks.
Leak and failure prediction in UWNs.

Water quality management

Impact of network operation and management on water quality.
New approaches for water quality analysis and monitoring.
Emerging pollutants: monitoring and mitigation.

Case studies and practices

Real-world examples of successful management and optimization projects.
Best management practices in water systems’ management and optimization.

Economic and social implications

Social impacts of interventions in UWNs.
Regulations in urban water management.
Customers’ willingness to pay analysis.
Stakeholders’ analysis and engagement in UWN management.
Resource mobilization for developing water infrastructures.

Future research directions

Emerging trends and future research directions in UWN management.

Other themes

Demand prediction in water distribution networks.
Operation and management of intermittent water systems.
Climate change effects on the operation and management of UWNs.
Analysis and control of transient flows in water systems.
Decentralized UWNs.

By bringing together these topics, this Special Issue offers valuable insights into UWN optimization and management. We invite contributions that address these areas and advance our collective understanding of this critical area.

We look forward to your valuable submissions.

Dr. Mohsen Hajibabaei
Prof. Dr. Robert Sitzenfrei
Prof. Dr. Mohsen Shahandashti
Dr. Milad Latifi
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 distribution
  • urban drainage
  • stormwater networks
  • sewer networks
  • optimization
  • resilience
  • sustainable management
  • water quality
  • critical infrastructure
  • technological innovations

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

22 pages, 4329 KB  
Article
Fractal-Based Approach to Simultaneous Layout Routing and Pipe Sizing of Water Supply Networks
by Paweł Suchorab, Dariusz Kowalski and Małgorzata Iwanek
Water 2025, 17(18), 2745; https://doi.org/10.3390/w17182745 - 17 Sep 2025
Viewed by 259
Abstract
The process of designing water distribution networks is divided into two main stages: network layout routing and pipe sizing. However, routing and sizing are not separate tasks—the shape of the network affects the diameters of the pipes, and vice versa. This paper presents [...] Read more.
The process of designing water distribution networks is divided into two main stages: network layout routing and pipe sizing. However, routing and sizing are not separate tasks—the shape of the network affects the diameters of the pipes, and vice versa. This paper presents an innovative fractal-based method, which enables the simultaneous layout routing and pipe sizing of water supply networks. The developed pipe routes and diameters selected according to the method are mathematically justified; the selection considers the total length of the pipes, the number of rotation angles of the base section, the cost of the water supply system construction and the priority of water supply to individual customers. The novelty of the method lies in the possibility of carrying out the processes of routing and sizing of the network in a recursive manner by the adoption of the principles of fractal geometry and Murray’s law. The method was tested under the conditions of a synthetic settlement. The obtained results enable us to conclude that the method is universal and suitable for shaping water supply networks, while determining the pipes’ diameters, both under the conditions of a single- and multi-sided water supply source. Full article
(This article belongs to the Special Issue Advances in Management and Optimization of Urban Water Networks)
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18 pages, 1127 KB  
Article
Comparative Analysis of Machine Learning Techniques in Enhancing Acoustic Noise Loggers’ Leak Detection
by Samer El-Zahab, Eslam Mohammed Abdelkader, Ali Fares and Tarek Zayed
Water 2025, 17(16), 2427; https://doi.org/10.3390/w17162427 - 17 Aug 2025
Viewed by 1105
Abstract
Urban areas face a significant challenge with water pipeline leaks, resulting in resource wastage and economic consequences. The application of noise logger sensors, integrated with ensemble machine learning, emerges as a promising real-time monitoring solution, enhancing efficiency in Water Distribution Networks (WDNs) and [...] Read more.
Urban areas face a significant challenge with water pipeline leaks, resulting in resource wastage and economic consequences. The application of noise logger sensors, integrated with ensemble machine learning, emerges as a promising real-time monitoring solution, enhancing efficiency in Water Distribution Networks (WDNs) and mitigating environmental impacts. The paper investigates the integrated use of Noise Loggers with machine learning models, including Support Vector Machines (SVMs), Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LogR), Multi-Layer Perceptron (MLP), and YamNet, along with ensemble models, for effective leak detection. The study utilizes a dataset comprising 2110 sound signals collected from various locations in Hong Kong through wireless acoustic Noise Loggers. RF model stands out with 93.68% accuracy, followed closely by KNN at 93.40%, and MLP with 92.15%, demonstrating machine learning’s potential in scrutinizing acoustic signals. The ensemble model, combining these diverse models, achieves an impressive 94.40% accuracy, surpassing individual models and YamNet. The comparison of various machine learning models provides researchers with valuable insights into the use of machine learning for leak detection applications. Additionally, this paper introduces a novel method to develop a robust ensemble leak detection model by selecting the most performing machine learning models. Full article
(This article belongs to the Special Issue Advances in Management and Optimization of Urban Water Networks)
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Graphical abstract

22 pages, 4621 KB  
Article
Probabilistic Forecasting and Anomaly Detection in Sewer Systems Using Gaussian Processes
by Mohsen Rezaee, Peter Melville-Shreeve and Hussein Rappel
Water 2025, 17(16), 2357; https://doi.org/10.3390/w17162357 - 8 Aug 2025
Viewed by 531
Abstract
This study investigates the capability of Gaussian process regression (GPR) models in the probabilistic forecasting of water flow and depth in a combined sewer system. Traditionally, deterministic methods have been implemented in sewer flow forecasting and anomaly detection, two crucial techniques for a [...] Read more.
This study investigates the capability of Gaussian process regression (GPR) models in the probabilistic forecasting of water flow and depth in a combined sewer system. Traditionally, deterministic methods have been implemented in sewer flow forecasting and anomaly detection, two crucial techniques for a good wastewater network and treatment plant management. However, with the uncertain nature of the factors impacting on sewer flow and depth, a probabilistic approach which takes uncertainties into account is preferred. This research introduces a novel use of GPR in sewer systems for real-time control and forecasting. To this end, a composite kernel is designed to capture flow and depth patterns in dry- and wet-weather periods by considering the underlying physical characteristics of the system. The multi-input, single-output GPR model is evaluated using root mean square error (RMSE), coverage, and differential entropy. The model demonstrates high predictive accuracy for both treatment plant inflow and manhole water levels across various training durations, with coverage values ranging from 87.5% to 99.4%. Finally, the model is used for anomaly detection by identifying deviations from expected ranges, enabling the estimation of surcharge and overflow probabilities under various conditions. Full article
(This article belongs to the Special Issue Advances in Management and Optimization of Urban Water Networks)
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25 pages, 5306 KB  
Article
Challenges of Urban Water Cycle Management in Small Spanish Municipalities: The Case of the Province of Granada
by Francisco Javier García-Martínez, Francisco Osorio and Francisco González-Gómez
Water 2025, 17(12), 1750; https://doi.org/10.3390/w17121750 - 10 Jun 2025
Viewed by 989
Abstract
Urban water service management in Spain presents two very distinct realities: populated service areas and small population centres. Despite the professionalised management of resources to provide a comprehensive, high-quality service in the largest service areas, small population centres face significant deficits and shortcomings [...] Read more.
Urban water service management in Spain presents two very distinct realities: populated service areas and small population centres. Despite the professionalised management of resources to provide a comprehensive, high-quality service in the largest service areas, small population centres face significant deficits and shortcomings that pose a major challenge for the Spanish public administration. This article reviews the existing problems surrounding the management of urban water cycle services in small-population municipalities in the province of Granada. This case study describes a reality that can be extrapolated to a significant number of small municipalities in the rest of Spain, where the management of urban water cycle services is directly assumed by City Councils. Having reviewed the problems, the article concludes with a series of recommendations for improving urban water cycle management in small municipalities. The conclusions emphasise the study and creation of optimal service areas, as well as the creation of an independent regulatory body. Full article
(This article belongs to the Special Issue Advances in Management and Optimization of Urban Water Networks)
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33 pages, 4434 KB  
Article
Developing Machine Learning Models for Optimal Design of Water Distribution Networks Using Graph Theory-Based Features
by Iman Bahrami Chegeni, Mohammad Mehdi Riyahi, Amin E. Bakhshipour, Mohamad Azizipour and Ali Haghighi
Water 2025, 17(11), 1654; https://doi.org/10.3390/w17111654 - 29 May 2025
Cited by 1 | Viewed by 1676
Abstract
This study presents an innovative data-driven approach to optimally design water distribution networks (WDNs). The methodology comprises five key stages: Generation of 600 synthetic WDNs with diverse properties, optimized to determine optimal component diameters; Extraction of 80 topological and hydraulic features from the [...] Read more.
This study presents an innovative data-driven approach to optimally design water distribution networks (WDNs). The methodology comprises five key stages: Generation of 600 synthetic WDNs with diverse properties, optimized to determine optimal component diameters; Extraction of 80 topological and hydraulic features from the optimized WDNs using graph theory; preprocessing and preparing the extracted features using established data science methods; Application of six feature selection methods (Variance Threshold, k-best, chi-squared, Light Gradient-Boosting Machine, Permutation, and Extreme Gradient Boosting) to identify the most relevant features for describing optimal diameters; and Integration of the selected features with four machine learning models (Random Forest, Support Vector Machine, Bootstrap Aggregating, and Light Gradient-Boosting Machine), resulting in 24 ensemble models. The Extreme Gradient Boosting-Light Gradient-Boosting Machine (Xg-LGB) model emerged as the optimal choice, achieving R2, MAE, and RMSE values of 0.98, 0.017, and 0.02, respectively. When applied to a benchmark WDN, this model accurately predicted optimal diameters, with R2, MAE, and RMSE values of 0.94, 0.054, and 0.06, respectively. These results highlight the developed model’s potential for the accurate and efficient optimal design of WDNs. Full article
(This article belongs to the Special Issue Advances in Management and Optimization of Urban Water Networks)
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16 pages, 9459 KB  
Article
Key Calibration Strategies for Mitigation of Water Scarcity in the Water Supply Macrosystem of a Brazilian City
by Jefferson S. Rocha, José Gescilam S. M. Uchôa, Bruno M. Brentan and Iran E. Lima Neto
Water 2025, 17(6), 883; https://doi.org/10.3390/w17060883 - 19 Mar 2025
Cited by 1 | Viewed by 779
Abstract
This study focuses on Fortaleza, the largest metropolis in Brazil’s semi-arid region. Due to recurrent droughts, massive infrastructure like high-density reservoir networks, inter-municipal and interstate water transfer systems, and a seawater desalination plant have been implemented to ensure the city’s water security. To [...] Read more.
This study focuses on Fortaleza, the largest metropolis in Brazil’s semi-arid region. Due to recurrent droughts, massive infrastructure like high-density reservoir networks, inter-municipal and interstate water transfer systems, and a seawater desalination plant have been implemented to ensure the city’s water security. To evaluate the quantitative and qualitative impact of introducing these diverse water sources into Fortaleza’s water supply macrosystem, adequate calibration of the operating and demand parameters is required. In this study, the macrosystem was calibrated using the Particle Swarm Optimization (PSO) method based on hourly data from 50 pressure head monitoring points and 40 flow rate monitoring points over two typical operational days. The calibration process involved adjusting the operational rules of typical valves in large-scale Water Distribution Systems (WDS). After parameterization, the calibration presented the following results: R2 of 88% for pressure head and 96% for flow rate, with average relative errors of 13% for the pressure head and flow rate. In addition, with NSE values above 0.80 after calibration for the flow rate and pressure head, the PSO method suggests a significant improvement in the simulation model’s performance. These results offer a methodology for calibrating real WDS to simulate various water injection scenarios in the Fortaleza macrosystem. Full article
(This article belongs to the Special Issue Advances in Management and Optimization of Urban Water Networks)
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19 pages, 3287 KB  
Article
A Method to Determine the Design Flow of Stormwater Pipe Networks Based on Dynamic Wave Simulation
by Ying Tang, Yujie Zhao, Zhaoguang Li, Jinjun Zhou and Hao Wang
Water 2024, 16(23), 3532; https://doi.org/10.3390/w16233532 - 8 Dec 2024
Viewed by 1095
Abstract
The rational method (RM) adopting steady uniform flow assumption is a simple and mainstream approach for the design of urban stormwater-drainage systems (USDSs). However, when designing large-scale USDSs, the RM significantly deviates from the actual flow regime due to inappropriate assumptions. To improve [...] Read more.
The rational method (RM) adopting steady uniform flow assumption is a simple and mainstream approach for the design of urban stormwater-drainage systems (USDSs). However, when designing large-scale USDSs, the RM significantly deviates from the actual flow regime due to inappropriate assumptions. To improve the accuracy and reliability of the design method, a dynamic-wave-simulation-based method (DWSBM) is proposed. Firstly, a numerical model which is equivalent to the surface runoff yield process of RM is established and validated. Then the dynamic wave module is adopted for pipe flow calculations. This method integrates hydraulic models for the whole design process. Both DWSBM and RM are used for USDS design of two areas, and design comparison demonstrates that the design flow rates computed using DWSBM are greater than obtained by RM. With the increase in pipeline length, the design flow differences between the two methods in the two areas increased from 3.09% to 28.97% and from 16.01% to 45.40% respectively. Adopting the DWSBM for design flow rate calculation can effectively improve the design reliability and drainage capacity of USDSs. Full article
(This article belongs to the Special Issue Advances in Management and Optimization of Urban Water Networks)
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