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

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

Deadline for manuscript submissions: 25 July 2025 | Viewed by 1987

Special Issue Editors

Dr. Katarzyna Pietrucha-Urbanik

E-Mail Website1 Website2
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

E-Mail Website1 Website2
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

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

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

Related Special Issues

Published Papers (4 papers)

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Research

23 pages, 4943 KiB  
Article
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
Viewed by 262
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Flowchart of proposed methodology.</p> Full article ">Figure 2
<p>Location of study area.</p> Full article ">Figure 3
<p>Fuzzy logic system.</p> Full article ">Figure 4
<p>Structure of hierarchical FL model for prediction of groundwater vulnerability to potential pollution.</p> Full article ">Figure 5
<p>MFs of each parameter: (<b>a</b>) depth to water table, (<b>b</b>) net recharge, (<b>c</b>) aquifer media, (<b>d</b>) soil media, (<b>e</b>) topography, (<b>f</b>) impact of vadose zone, (<b>g</b>) hydraulic conductivity, and (<b>h</b>) groundwater vulnerability index.</p> Full article ">Figure 6
<p>Spatial distributions of parameters describing groundwater vulnerability to contamination in the study area: (<b>a</b>) depth to the water table (mgbl), (<b>b</b>) recharge rate (Piscopo method), (<b>c</b>) aquifer media, (<b>d</b>) soil media, (<b>e</b>) topography (slope%), (<b>f</b>) impact of vadose zone, and (<b>g</b>) hydraulic conductivity (m/day).</p> Full article ">Figure 7
<p>Interpolated groundwater vulnerability index from hierarchical FIS model outputs.</p> Full article ">Figure 8
<p>R<sup>2</sup> values of FGWVI against measured EC and nitrate concentrations.</p> Full article ">
22 pages, 6539 KiB  
Article
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
Viewed by 248
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 [...] Read more.
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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Figure 1

Figure 1
<p>The total length of China’s urban water supply pipeline from 2010 to 2022.</p> Full article ">Figure 2
<p>Pressure signal.</p> Full article ">Figure 3
<p>Leak location principle based on TDOA.</p> Full article ">Figure 4
<p>Structure of CNN.</p> Full article ">Figure 5
<p>Structure of GRU.</p> Full article ">Figure 6
<p>Structure of the ATT mechanism.</p> Full article ">Figure 7
<p>CNN-GRU-ATT pipeline leak recognition model.</p> Full article ">Figure 8
<p>Topology of the experimental platform structure.</p> Full article ">Figure 9
<p>Experimental platform.</p> Full article ">Figure 10
<p>Pressure signals of four working conditions after noise reduction.</p> Full article ">Figure 11
<p>Training process of CNN-GRU-ATT.</p> Full article ">Figure 12
<p>Accuracy and loss changes of each network.</p> Full article ">Figure 13
<p>Noise reduction signal and IMFs.</p> Full article ">Figure 14
<p>Noise reduction signal and its low frequency components.</p> Full article ">Figure 15
<p>Noise reduction NPW signals and its high-frequency component.</p> Full article ">Figure 16
<p>The noise reduction NPW signals <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and high frequency component.</p> Full article ">
22 pages, 7687 KiB  
Article
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
Viewed by 597
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 [...] Read more.
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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Figure 1

Figure 1
<p>General framework diagram.</p> Full article ">Figure 2
<p>Layout of raw water pipeline test section.</p> Full article ">Figure 3
<p>Installation of the experimental device.</p> Full article ">Figure 4
<p>Pressure fluctuation diagram of the raw water pipeline, The red rectangle highlights the leakage signal, while the green rectangle indicates normal fluctuations resembling leakage.</p> Full article ">Figure 5
<p>Laboratory equipment. (<b>a</b>) Overview diagram of the experimental pipeline system; (<b>b</b>) actual image of the experimental pipeline system.</p> Full article ">Figure 6
<p>NPW signals. (<b>a</b>) NPW signal collected from laboratory pipeline; (<b>b</b>) NPW signal collected from raw water pipeline.</p> Full article ">Figure 7
<p>Encoder architecture.</p> Full article ">Figure 8
<p>Shift window input.</p> Full article ">Figure 9
<p>Parameter-based transformer–TL training.</p> Full article ">Figure 10
<p>Positioning schematic diagram.</p> Full article ">Figure 11
<p>Simulation signal positioning results.</p> Full article ">Figure 12
<p>Delay caused by different causes.</p> Full article ">Figure 13
<p>Confusion matrices. (<b>a</b>) Confusion matrix of the model on the experimental pipeline dataset; (<b>b</b>) confusion matrix of the model on the raw water pipeline dataset.</p> Full article ">Figure 14
<p>The inflection point located by the proposed method.</p> Full article ">
20 pages, 4503 KiB  
Article
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
Viewed by 627
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 [...] Read more.
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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Figure 1

Figure 1
<p>Map of neighbourhoods of the City of Vancouver, BC, Canada. Base map data © OpenStreetMap contributors.</p> Full article ">Figure 2
<p>Scree plot of eigenvalues and associated components.</p> Full article ">Figure 3
<p>Cumulative variance plot and associated components.</p> Full article ">Figure 4
<p>Social vulnerability index (SVI) class of each dissemination area in Vancouver, from Very Low to Very High. Base map data © OpenStreetMap contributors, rendered using Plotly (<a href="http://plotly.com" target="_blank">http://plotly.com</a>, accessed on 1 May 2023).</p> Full article ">Figure 5
<p>Environmental vulnerability index (EVI) class of each dissemination area in Vancouver, from Very Low to Very High. Base map data © OpenStreetMap contributors, rendered using Plotly (<a href="http://plotly.com" target="_blank">http://plotly.com</a>, accessed on 1 May 2023).</p> Full article ">Figure 6
<p>Economic Vulnerability Index (ECI) class of pipes in Vancouver, from Low to Very High (Note: Very Low ECI is not shown to facilitate the visualization of more vulnerable pipes). Base map data © OpenStreetMap contributors, rendered using Plotly (<a href="http://plotly.com" target="_blank">http://plotly.com</a>, accessed on 1 May 2023).</p> Full article ">Figure 7
<p>Consequence of failure (COF) class of pipes in Vancouver, from Low to Very High (Note: Very Low COF is not shown to facilitate the visualization of more vulnerable pipes). Base map data © OpenStreetMap contributors, rendered using Plotly (<a href="http://plotly.com" target="_blank">http://plotly.com</a>, accessed on 1 May 2023).</p> Full article ">Figure 8
<p>Fitting curve of pipe break frequency and Weibull PDF.</p> Full article ">Figure 9
<p>Risk class of pipes in Vancouver, from Low to Very High (Note: Very Low Risk is not shown to facilitate the visualization of high-risk pipes). Base map data © OpenStreetMap contributors, rendered using Plotly (<a href="http://plotly.com" target="_blank">http://plotly.com</a>, accessed on 1 May 2023).</p> Full article ">
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