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Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition

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

Deadline for manuscript submissions: closed (15 February 2026) | Viewed by 17444

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Special Issue Editors

Dr. Katarzyna Pietrucha-Urbanik

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Guest Editor

Department of Water Supply and Sewerage Systems, Faculty of Civil, Environmental Engineering and Architecture, Rzeszow University of Technology, 35-959 Rzeszow, Poland
Interests: reliability and safety of municipal systems; water supply systems; water network; risk analysis connected with water supply systems operation; safety of water supply consumers; failure risk analysis; reliability-based risk assessment
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Janusz Rak

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Guest Editor

Department of Water Supply and Sewerage Systems, Faculty of Civil, Environmental Engineering and Architecture, Rzeszow University of Technology, 35-959 Rzeszow, Poland
Interests: critical infrastructure; reliability and safety; water supply systems; consumers; failure; risk analysis; reliability-based risk assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The reliability and safety of engineering systems are permanent scientific and operational issues. They become even more pressing issues if these engineering systems belong to critical infrastructures. Water supply systems are part of the critical infrastructure of modern societies. The first mission of a water supply system is to provide households with potable water in the required quantity, at the appropriate pressure, and on demand, as required by statutory regulations. Risk assessments are primarily focused on supply disruption risk (shortage or deficit) and its consequences on the environment, consumer health, and the global security of the city. Examinations of the current operational state, potential major threats, and the related hazards should all be part of every risk assessment. The proposed approaches are meant to address a wide spectrum of water supply system reliability, resilience, safety, and risk modelling, as well as assessment issues.

Dr. Katarzyna Pietrucha-Urbanik
Prof. Dr. Janusz Rak
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.

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Keywords

artificial inteligence
contamination
crisis situation
cybersecurity in water supply systems
data-driven decision-making
digital twin
diversification
failure risk analysis
hazard identification
innovative methodologies
IoT
machine learning
manage drinking water supply safety
matrix
neural networks
optimal network design
prediction models
reliability-based risk assessment
resilience
risk analysis
risk and vulnerability assessment
risk assessment methodology
safety
smart metering
techniques and technology for smart water systems
the rehabilitation of water distribution networks
the safety of water supply systems
water demand modeling
water distribution networks
water–energy nexus
water losses
water network failure analysis
water quality
water quality monitoring
water safety plans
water supply systems
water treatment

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Related Special Issues

Water Supply System (WSS) Reliability, Safety and Risk Modelling & Assessment in Water (12 articles)
Water Supply System Reliability, Safety and Risk Modelling & Assessment, Volume II in Water (11 articles)
Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 4th Edition in Water

Published Papers (10 papers)

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Editorial

Jump to: Research, Other

3 pages, 120 KB

Open AccessEditorial

Artificial Intelligence, Leak Detection, Water Losses and Cybersecurity in Water Supply Systems

by Katarzyna Pietrucha-Urbanik and Janusz Rak

Water 2026, 18(10), 1144; https://doi.org/10.3390/w18101144 - 11 May 2026

Viewed by 302

Abstract

The third edition of this Special Issue appears at a time when water utilities are being reshaped by digitalisation, climate variability, ageing infrastructure and rising expectations regarding service continuity [...] Full article

(This article belongs to the Special Issue Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition)

Research

Jump to: Editorial, Other

26 pages, 6310 KB

Open AccessArticle

Hydrochemical Characterization and Origins of Groundwater in the Semi-Arid Batna Belezma Region Using PCA and Supervised Machine Learning

by Zineb Mansouri, Abdeldjalil Belkendil, Haythem Dinar, Hamdi Bendif, Anis Ahmad Chaudhary, Ouafa Tobbi and Lotfi Mouni

Water 2026, 18(8), 969; https://doi.org/10.3390/w18080969 - 19 Apr 2026

Viewed by 490

Abstract

In the semi-arid Batna Belezma region of northeastern Algeria, groundwater is a vital resource for agriculture and drinking water. However, the climate leads to intense evaporation, which affects its quality. This study aims to identify the key hydrogeochemical processes that control groundwater composition in the Merouana Basin and to evaluate the predictive performance of machine learning (ML) models. A total of 30 groundwater samples were analyzed using multivariate statistical techniques, including Principal Component Analysis (PCA), and were modeled using PHREEQC to assess mineral saturation states. Additionally, ML-based regression models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB),were employed to predict groundwater chemistry. The results indicate that the dominant ion distribution follows the following trend: Ca²⁺ > Mg²⁺ > Na⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻. Alkaline earth metals (Ca²⁺ and Mg²⁺) constitute the major fraction of total dissolved cations, reflecting carbonate equilibrium and dolomite dissolution processes. In contrast, Na⁺ represents a smaller proportion of the cationic load; however, its hydro-agronomic significance is substantial due to its influence on sodium adsorption ratio (SAR) and soil permeability. The PHREEQC modeling showed that calcite and dolomite precipitation promote evaporite dissolution, while most samples remain undersaturated with respect to gypsum. The PCA results reveal high positive loadings of Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and EC, suggesting that ion exchange and seawater mixing are the primary controlling processes, with carbonate weathering playing a secondary role. To enhance predictive assessment, several supervised machine learning models were tested. Among them, the Random Forest model achieved the highest predictive performance (R² = 0.96) with low RMSE and MAE values, confirming its robustness and reliability. The results indicate that silicate weathering and mineral dissolution are the primary mechanisms governing groundwater chemistry. The integration of multivariate statistics and machine learning provides a comprehensive understanding of groundwater evolution and offers a reliable predictive framework for sustainable water resource management in semi-arid environments. Geochemical model performance showed a high global accuracy (GPI = 0.91), confirming a strong agreement between observed and simulated chemical data. However, the HH value (0.81) indicates some discrepancies, particularly for specific ions or extreme conditions. Full article

(This article belongs to the Special Issue Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition)

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15 pages, 1056 KB

Open AccessArticle

The Financial Burden of Boil Water Advisories on Public Water Utilities

by Fahad Alzahrani and Rady Tawfik

Water 2026, 18(7), 770; https://doi.org/10.3390/w18070770 - 24 Mar 2026

Viewed by 358

Abstract

Aging drinking water infrastructure and persistent underinvestment have increased the frequency of service disruptions across public water systems in the United States, yet empirical evidence on the financial implications of such disruptions for water utilities remains limited. This study examines the relationship between boil water advisory (BWA) exposure and operating costs incurred by public water utilities using a cross-sectional dataset of 239 publicly owned community water systems in West Virginia during the 2023 fiscal year. Utility costs are measured using operating revenue deductions, an accounting measure capturing operating expenses, taxes, and depreciation. Regression results indicate a statistically significant positive association between cumulative BWA exposure and utility costs. Specifically, a one-day increase in advisory exposure is associated with approximately a 0.08% increase in operating deductions, implying an average cost increase of $1020 per utility for each day under advisory. Duration-based measures of BWA exposure explain cost variation more consistently than simple advisory counts, highlighting the importance of capturing persistence rather than frequency alone. These findings demonstrate that service reliability disruptions impose financial burdens on public water utilities and highlight the need to incorporate reliability considerations into infrastructure investment decisions, rate setting, and long-term financial planning, particularly for small and resource-constrained systems. Full article

(This article belongs to the Special Issue Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition)

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26 pages, 724 KB

Open AccessArticle

Risk Analysis and Assessment of Water Supply Projects Using the Fuzzy DEMATEL-ANP and Artificial Neural Network Methods

by Mohammad Khalilzadeh, Sayyid Ali Banihashemi, Ali Heidari, Darko Božanić and Aleksandar Milić

Water 2025, 17(13), 1995; https://doi.org/10.3390/w17131995 - 2 Jul 2025

Cited by 7 | Viewed by 1630

Abstract

Today, companies face complexities and uncertainties that make it difficult to manage various risks. One of the important tools for achieving success in water supply projects is the proper implementation of risk management processes and activities throughout the project’s make-span. Risk identification and assessment are two important steps in project risk management. In this research, the Fuzzy DEMATEL and Fuzzy ANP as well as Artificial Neural Network methods are exploited for the analyzing and ranking of environmental risks of water supply projects. Risks are classified and then prioritized by the Fuzzy ANP and Artificial Neural Network methods into four main categories, including technical, organizational, project management, and external risks. The weight of each of the technical, organizational, project management, and external risks using the ANP method was obtained as 0.31, 0.26, 0.25, and 0.18, respectively, and the following weights were obtained using the Artificial Neural Network: 0.42, 0.27, 0.22, and 0.09, respectively. The results show that although the exact weights differed between methods, especially for technical and external risks, the overall prioritization of risk categories followed a broadly consistent pattern. In addition, the risk associated with the suppliers obtained the highest weight among the external risks; the risk associated with the high cost of materials gained the highest weight among the organizational risks; the risk associated with the requirements acquired the highest weight among the technical risks; and finally, the risk associated with communication achieved the highest weight among the project management risks. The method presented in this research helps project managers and decision-makers in the water supply industry to make a better and more realistic risk assessment by considering the mutual effects of project risks. Full article

(This article belongs to the Special Issue Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition)

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19 pages, 3471 KB

Open AccessArticle

Quantitative Assessment of the Reliability of Water Treatment Plant as an Example of Anthropotechnical System

by Jakub Żywiec, Barbara Tchórzewska-Cieślak and Janusz Rak

Water 2025, 17(12), 1742; https://doi.org/10.3390/w17121742 - 9 Jun 2025

Viewed by 1222

Abstract

The anthropotechnical system is a system of human–technical object–environment. In addition to the reliability of technical objects, the reliability of humans is also important in the proper functioning of such a system. The water supply system is an example of an anthropotechnical system. The operator of such a system is responsible, among other things, for the proper functioning of the water treatment plant, in which the production of water takes place. The operator’s task is to control individual subsystems and technological objects in order to supply water of the right quality, in the right quantity, at the required pressure to the consumer at any time, without making an error. The work shows the reliability of the anthropotechnical system assessment using the example of a water treatment plant located in south-eastern Poland. The single-parameter method and the reliability index were used to analyze the reliability. The reliability of the tested object was analyzed for the technical and anthropotechnical variant. The results indicate that after taking into account the influence of the operator on the reliability of the tested system, a decrease in the reliability index of 11% is observed. In order to minimize the negative influence of human factor on the reliability of the anthropotechnical system, it is recommended to duplicate the system operator, which allows for increasing the level of reliability of the anthropotechnical system. Full article

(This article belongs to the Special Issue Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition)

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23 pages, 4943 KB

Open AccessArticle

Assessment and Validation of Shallow Groundwater Vulnerability to Contamination Based on Fuzzy Logic and DRASTIC Method for Sustainable Groundwater Management in Southeast Hungary

by Abdelouahed Fannakh, Barta Károly, Mhamed Fannakh and Andrea Farsang

Water 2025, 17(5), 739; https://doi.org/10.3390/w17050739 - 3 Mar 2025

Cited by 9 | Viewed by 2374

Abstract

A hierarchical fuzzy inference system (FIS) integrated with the DRASTIC model is applied in this study to enhance the assessment of shallow groundwater vulnerability in southeast Hungary, a region characterized by extensive agriculture and industrial growth. Traditional groundwater vulnerability models often struggle with parameter imprecision and uncertainty, affecting their reliability. To address these limitations, fuzzy logic was incorporated to refine the classification of vulnerability zones. The hierarchical FIS incorporates the seven DRASTIC parameters: depth to the water table, net recharge, aquifer media, soil media, topography, vadose zone impact, and hydraulic conductivity, assigning flexible ratings through fuzzy membership functions. The model classifies the fuzzy groundwater vulnerability index (FGWVI) into low, moderate, and high categories, revealing that 63.9% of the study area is highly susceptible to contamination, particularly in regions with shallow water tables and sandy soils. Validation was conducted using nitrate (NO₃⁻) concentrations and electrical conductivity (EC) measurements from 46 agricultural wells to assess the correlation between predicted vulnerability zones and actual groundwater quality indicators. The correlation analysis revealed a moderately strong positive relationship between FGWVI and both NO₃⁻ (R² = 0.4785) and EC (R² = 0.528), supporting the model’s ability to identify high-risk contamination zones. This study highlights the effectiveness of the fuzzy-enhanced DRASTIC model in evaluating aquifer vulnerability and provides crucial insights to assist policymakers in identifying pollution sources and developing strategies to mitigate groundwater contamination, thereby alleviating the stress on this critical resource. Full article

(This article belongs to the Special Issue Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition)

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22 pages, 6539 KB

Open AccessArticle

Research on Application of Convolutional Gated Recurrent Unit Combined with Attention Mechanism in Water Supply Pipeline Leakage Identification and Location Method

by Zhu Jiang, Yuchen Wang, Haiyan Ning and Yao Yang

Water 2025, 17(4), 575; https://doi.org/10.3390/w17040575 - 17 Feb 2025

Cited by 4 | Viewed by 1346

Abstract

To improve the accuracy of leak identification and location of water supply pipelines, a novel convolution gated recurrent unit method based on the attention mechanism is proposed in this paper. Firstly, a convolutional neural network is used to capture the localspatio-temporal characteristics of the signal. Secondly, a gated recurrent unit is used to extract the signal’s long dependence relationship. Finally, an attention mechanism is combined to highlight the influence of key features in the learning process, so as to achieve accurate recognition of the pipeline pressure state. The accurate identification of leakage faults is expected to further improve the location accuracy of pipeline leakage points, which is very important for the practical application of the algorithm in engineering. In order to verify the effectiveness of the proposed method, a simulated leakage test platform is set up for the leakage simulation test. The test results of different leakage conditions show that the recognition accuracy of the proposed network structure is 98.75% for test samples, which is higher than other network structures of the same type. According to the identification results of leakage characteristics, the VMD method is used to extract the high-frequency components of the negative pressure wave signal, so as to obtain the inflection point of the negative pressure wave, so as to determine the arrival time difference of the signal, and the arrival time method based on the negative pressure wave is used to locate the leakage point. Across 12 leak locations, the maximum relative error is 7.67%, the minimum relative error is 0.86%, and the average relative error is only 2.97%, achieving the best performance among the various methods. The positioning accuracy meets the requirement of practical application and the algorithm has good robustness. Full article

(This article belongs to the Special Issue Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition)

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22 pages, 7687 KB

Open AccessArticle

Water Pipeline Leak Detection Method Based on Transfer Learning

by Jian Cheng, Zhu Jiang, Hengyu Wu and Xiang Zhang

Water 2025, 17(3), 368; https://doi.org/10.3390/w17030368 - 28 Jan 2025

Cited by 3 | Viewed by 5500

Abstract

In order to improve the accuracy of leakage detection in water pipelines, this paper proposes a novel method based on Transformer and transfer learning. A laboratory test platform was established to obtain datasets with rich leakage characteristics. An enhanced feature extraction technique using a shift window input method mapped the NPW sequences into embedding vectors, effectively capturing the fine-grained features while reducing the sequence length, thereby enhancing the Transformer’s retention of sequence details. An improved Transformer encoder was pre-trained on the Experimental pipeline dataset and refined with limited leakage data from real pipelines for accurate detection. Additionally, a novel signal difference-based method was introduced for precise leak localization. The pressure signal was denoised, and the inflection points were identified by subtracting two signals. The points between the inflection and lowest signal points were traversed, with slope calculations optimizing the time delay computations. A leakage simulation test was conducted on a section of a raw water pipeline in Shanghai, and the test results confirmed the effectiveness of these methods. A 100% detection rate, zero false alarms, and a relative positioning error of less than 3.14% were achieved on a test set of 45 instances. Full article

(This article belongs to the Special Issue Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition)

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20 pages, 4503 KB

Open AccessEditor’s ChoiceArticle

Holistic Assessment of Social, Environmental and Economic Impacts of Pipe Breaks: The Case Study of Vancouver

by Armine Sinaei, Rebecca Dziedzic and Enrico Creaco

Water 2025, 17(2), 252; https://doi.org/10.3390/w17020252 - 17 Jan 2025

Cited by 4 | Viewed by 2233

Abstract

This paper presents a holistic assessment framework for the impacts of water distribution pipe breaks to promote environmentally sustainable and socially resilient cities. This framework considers social, environmental, and economic vulnerabilities as well as probabilities associated with pipe failure. The integration of these features provides a comprehensive approach to understanding infrastructure risks. Taking the city of Vancouver as a case study, the social vulnerability index (SVI) is obtained following the application of a cross-correlation matrix and principal component analysis (PCA) to identify the most influential among 33 selected variables from the 2021 census of the Canadian population. The Environmental Vulnerability Index (EVI) is evaluated by considering the park and floodplain areas. The Economic Vulnerability Index (ECI) is derived from the replacement cost of pipes. These indices offer valuable insights into the spatial distribution of vulnerabilities (consequences) across urban areas. Subsequently, the Consequence of Failure (COF) is computed by aggregating the three vulnerabilities with equal weights. Pipe probability of failure (POF) is evaluated by a Weibull model calibrated on real break data as a function of pipe age. This approach enables a dynamic evaluation of pipe deterioration over time. Risk is finally assessed by combining COF and POF for prioritizing pipe replacement and rehabilitation, with the final objective of mitigating the adverse impacts of infrastructure failure. The findings show the significant impact of ethnicity, socioeconomic indices, and education on the social vulnerability index. Moreover, the areas close to English Bay and Fraser River are more environmentally vulnerable. The pipes with high economic vulnerability are primarily concrete pipes, due to their expensive replacement costs. Finally, the risk framework resulting from the vulnerabilities and pipe break probabilities is used to rank the Vancouver City water distribution network pipes. This ranking system highlights critical areas requiring different levels of attention for infrastructure improvements. All the pipes and corresponding risks are illustrated in Vancouver maps, highlighting that the pipes associated with a very high level of risk are mostly in the south and north of Vancouver. Full article

(This article belongs to the Special Issue Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition)

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Other

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23 pages, 2050 KB

Open AccessSystematic Review

Cybersecurity in Water Distribution Networks: A Systematic Review of AI-Based Detection Algorithms

by Md Arman Habib, Anca Delia Jurcut, Hafiz Ahmed, Wenhui Wei and Md Salauddin

Water 2026, 18(4), 519; https://doi.org/10.3390/w18040519 - 22 Feb 2026

Viewed by 909

Abstract

Water Distribution Networks (WDNs) are critical infrastructure for delivering clean and safe drinking water. As modern WDNs increasingly integrate cyber technologies, they evolve into complex cyber–physical systems (CPSs). This connectivity, however, introduces new vulnerabilities, including cyberattacks. Cybersecurity protects systems from unauthorized access, attacks, and data breaches. In this systematic review, we adopted the PRISMA 2020 reporting guideline. Predefined keyword strings were designed to extract relevant articles from Scopus and Web of Science during the period of 2014–2025. In total, 32 peer-reviewed studies were included for narrative synthesis following duplication and eligibility screening. The review protocol was not registered. This review provides a unified perspective on how Artificial Intelligence (AI) contributes to WDNs resilience. The literature is evaluated in terms of detection tasks, data modalities, learning paradigms, and model architecture. The results highlight three key findings: (a) data bias, reflected in significant reliance on specific synthetic datasets and limited use of real-world utility network data; (b) performance, with deep learning architecture, such as long-short-term memory models, achieving commendable levels of accuracy in intrusion detection, however, overall comparison with other models remain scenario-dependent; and (c) future directions, synthesized through an AI-centered perspective that emphasizes resilience and identifies research gaps in adaptive online learning, attack prediction, interpretability, federated learning and topology localization. This study concludes with recommendations for the broader integration of AI tools to support resilient WDN operation. Full article

(This article belongs to the Special Issue Water Supply System Reliability, Resilience, Safety and Risk Modelling & Assessment, 3rd Edition)

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