Reliability, Safety and Risk Modelling in Water Supply Systems for Climate-Resilient Infrastructure

Modern water supply systems comprise complex technical infrastructure, reacting to extreme user demands and ever-growing environmental pressure. The complexity of distribution networks, the unpredictability of failures, and the shortages and contamination of water mean that the reliability and safety of water supply infrastructure remain key challenges for cities and regions around the world. The second volume of this Special Issue of Water, devoted to reliability, safety, and risk modelling in water supply systems, shows how the application of a wide range of approaches utilising advanced data analysis methods to innovative treatment technologies can contribute to the reliability and resilience of these crucial systems. In light of accelerating climate variability and increasing hydrological extremes, strengthening the climate resilience of water supply systems has become an essential dimension of reliability and risk assessment, linking traditional engineering approaches with adaptive and forward-looking strategies.

The first group of contributions focuses on machine learning approaches and digital risk modelling. The two opening papers examine leakage risk in water distribution networks. Wu and co-authors (Contributions 1 and 2) proposed a multi-component attention-based solution that integrates a conditional GAN with data-balancing techniques. Tested on data from the B2 and B3 districts in Zhengzhou, the model improved the recall rate for detecting leak-prone pipeline segments by more than 40% and demonstrated that increasing representative data points significantly enhances predictive performance. The same team later developed a model using graph neural networks (GNN), integrating network topology, GIS variables, and historical failure records. The model identified key leak-related factors such as pipe age, material, and previous failures, outperforming established machine learning approaches. Together, these papers illustrate a global shift toward leveraging operational data and artificial intelligence to improve failure prediction in critical infrastructure.

Medeiros and co-authors (Contribution 3) further show that predictive models can be developed even under limited data availability. Using historical failure data from the Brazilian utility CAGEPA and multilayer perceptron neural networks, the authors predicted with over 80% accuracy when the next failure would occur in specific network segments within a 90-day horizon. By distinguishing between new and recurring events and incorporating variables such as terrain elevation and the history of previous breaks, the study highlights the importance of systematic data collection and analytics-driven maintenance planning.

Zhang and colleagues (Contribution 4) extended digital assessment to fire protection systems, developing an IoT-based platform for intelligent testing of fire pumps. Sensors monitor flow, pressure, power, and efficiency, transmitting data to an analytical module. The use of the discrete Fréchet distance to compare performance curves enables automatic detection of pump anomalies, while optimisation through genetic algorithms enhances discriminatory capability. This system demonstrates how IoT-supported diagnostics can increase the operational reliability of firefighting installations.

A second thematic group addresses energy-efficient design and strategic management of distribution systems. Ryu and Lee (Contribution 5) developed an improved life cycle energy analysis (LCEA) model for optimal WDS design, incorporating new pipe maintenance criteria and a resilience index based on nodal pressure. A revised unit energy formulation and modernised pipe replacement standards reduced total energy consumption by approximately 35% compared with models focused solely on cost minimisation. This demonstrates that energy-efficient design can simultaneously reduce emissions, improve hydraulic conditions, and increase resilience to failures.

Large-scale infrastructure interventions were explored in a case study by Song and co-authors (Contribution 6), analysing how future water transfer projects will reshape the supply network in the Erhai Lake region by 2035. Using landscape ecology methods combined with network theory, the authors assessed the connectivity of 215 nodes and 216 links in the 2020 network and predicted increases of 122 nodes and 163 links due to planned projects. These changes substantially strengthen overall system resilience. Increasing the number of nodes and decentralising flows enhances tolerance to failures, underscoring the importance of resource redistribution in mitigating climate-driven water inequalities.

Environmental factors and water quality also play a fundamental role in system reliability. Shrestha and colleagues(Contribution 7) evaluated the removal of iron and manganese in groundwater using dual-media filters filled with manganese-oxide-coated sand and ceramic. Traditional aeration and rapid sand filtration were insufficient for manganese removal, whereas the coated ceramic medium reduced concentrations from 1.10 mg/L to below 0.01 mg/L. Increasing pH from 7.5 to 9.0 improved stability. These findings are vital for regions affected by metal-rich groundwater, given the health risks associated with chronic manganese exposure.

Amin and co-authors (Contribution 8) analysed pipe scale deposition in Surakarta, Indonesia, revealing that groundwater-supplied zones exhibited higher dissolved solids, hardness, and alkalinity than surface water zones. Although traditional indices suggested moderate calcium carbonate scaling potential, deposit analysis showed a dominance of manganese (50–80%) and iron (46–64%). The deposits likely developed through oxidation by manganese-oxidising bacteria, exacerbated by the absence of groundwater chlorination. With an estimated deposition rate of 1660 kg per year, the study demonstrates that scaling indices alone are insufficient; mineralogical analysis is essential for accurate risk assessment.

Tahraoui and colleagues (Contribution 9) addressed polluted surface water sources, conducting a year-long evaluation of coagulation–flocculation treatment with aluminium sulphate. Frequent sampling and jar tests combined with SVM modelling enabled the determination of optimal coagulant dosage. Follow-up analyses after one year confirmed consistently high removal efficiencies, supporting the need for continuous improvement of treatment technologies, especially where surface water degradation poses major health risks.

Because water supply systems are anthropotechnical systems, technical performance is inseparable from human operation. Żywiec, Tchórzewska-Cieślak, and Sokolan (Contribution 10) analysed operator behaviour in water treatment plants using the Fuzzy-Bayes CREAM method. Estimated human error probabilities ranged from 0.0005 to 0.0746, depending on the process. The study emphasises that risk assessments incorporating human factors more accurately reflect operational conditions and that decision-support tools must accompany increasing automation to avoid new vulnerabilities.

Taken together, the contributions in this volume demonstrate that improving the reliability and safety of water supply systems requires an integrated, interdisciplinary approach. Combining engineering analysis, machine learning, IoT-based diagnostics, innovative filtration materials, and recognition of human factors allows water infrastructure to be managed more effectively.

Several common conclusions emerge:

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The reliability of predictive models depends on high-quality data and the integration of GIS, monitoring, and historical failure records;

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Energy-optimised design incorporating life cycle analysis and resilience indices yields ecological and operational benefits;

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Climate change and uneven water availability necessitate large transfer projects supported by system-level analyses;

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Effective treatment of iron, manganese, and other contaminants is essential for safeguarding public health;

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Human reliability assessment and operator training must accompany the digitalisation of water systems.

Looking ahead, a promising direction is the development of integrated smart water platforms combining IoT sensing, predictive analytics, and water quality monitoring. In the context of rapid urbanisation and climate change, ensuring safe and sustainable water supplies becomes a global priority. Solutions based on artificial intelligence, including digital twins enabling real-time simulation, together with strengthened cybersecurity frameworks, will be indispensable for future-ready water infrastructure.