Enhanced leak detection in water distribution systems using GFCC-based signal processing techniques

Augmenting water quality resilience in water distribution systems: A stress-driven model for ice slurry pigging optimisation strategy.

Towards collective intelligence in agriculture: Deep reinforcement learning and digital twins for efficient management of collective irrigation water distribution systems

Seismic fragility model for the system-level performance evaluation of water distribution systems

Multiscale mechanistic study of ammonia-driven chlorine speciation and control of bacteria and fungal spores in mixed chlorine/chloramines systems.

Exploration of deep learning leak detection model across multiple smart water distribution systems: Detectable leak sizes with AMI meters

The primary objective of water distribution systems (WDSs) is to ensure a high-quality water supply. Chlorine, commonly used as a primary disinfectant in WDSs, requires precise control of its concentration to safeguard public health. However, the complex network structure and its highly nonlinear dynamics inherent in a WDS pose significant challenges in chlorine management. This study proposes an optimization approach to tackle these challenges by leveraging different kinds of valves to distribute water flow within the network, thereby realizing both chlorine and pressure management. A simplified chlorine propagation model is introduced, based on which three optimization problems are formulated and solved to compute optimal management strategies. The first one is a nonlinear programming (NLP) which minimizes source dosing across the WDS by an optimal distribution of the water flow in the pipelines, leading to a lower bound for the chlorine management. The second one is a mixed-integer nonlinear programming (MINLP) problem to localize isolation valves (IVs) and achieve a realistic and practical solution. The third one extends the MINLP framework to integrate pressure reducing valves (PRVs), optimizing the placement of both IVs and PRVs to enable a multi-objective approach that minimizes chlorine dosing while regulating the system pressure. The results of a benchmark demonstrate that the integrated use of IVs and PRVs significantly enhances the performance of both chlorine concentration and pressure regulation, offering an efficient solution for WDS management.

Water disinfection accelerated rubber seal aging within water distribution system over varied conditions.

In recent years, Geographical Information Systems (GIS) and their associated databases have become essential tools in the management of water supply infrastructure. Their application in water utilities extends beyond public health protection and now plays a pivotal role in network operation, maintenance planning, and risk analysis. This study focuses on the integration of GIS tools, operational data, and failure records in the risk - based management of water distribution systems, with particular attention to minimizing disruptions in water supply to consumers. A fundamental requirement for reliable operation of a water supply system is detailed knowledge of its network structure, operating conditions, technical status, and historical data on system failures. Modern GIS platforms, especially when integrated with other digital tools such as SCADA systems, hydraulic models, and monitoring software, provide a robust framework for this. One of the most valuable GIS functionalities for both water suppliers and consumers is the systematic registration of failures in the water distribution network. Failure logs, compiled over several years, offer critical insights into the causes, frequency, and seasonality of breakdowns. These dataset s serve as the foundation for assessing infrastructure reliability and planning targeted maintenance interventions. This study presents an example of failure analysis conducted on a selected water supply network in Poland. The analysis highlights dominant failure causes and their temporal distribution. Using GIS - based numerical maps and failure databases, spatial distribution and intensity of pipe damage were evaluated. This facilitated the identification of high - risk areas and pipelines with elevated failure rates, which pose the greatest threat to continuous water supply. Risk mapping based on failure frequen cy and infrastructure condition supports decision - making in the allocation of repair resources and scheduling of rehabilitation works. This approach not only improves the effectiveness of maintenance teams but also reduces the risk of service interruptions. Moreover, the methodology is aligned with broader European policies such as the INSPIRE Directive, which promotes harmonized spatial data infrastructures as a basis for environmental and risk assessments. In an era where informatization drives operational efficiency, GIS and related information systems offer unmatched potential in the risk assessment and management of water distribution infrastructure. Their ability to process and visualize complex datasets transforms raw operational data into actionable intelligence. The outcome is a proactive maintenance strategy that enhances the resilience and security of water supply systems, ultimately ensuring uninterrupted service delivery to consumers.

Abstract The January 2025 wildfires devastated Los Angeles, claiming lives, homes, jobs, and whole communities. As the fires raged, discussions erupted across social and mainstream media, questioning whether water supply systems could have been more prepared to fight the fires. Especially reflecting the narratives and associated policymaker and public expectations around water supply systems fighting wildfires, the manuscript presents a description of proposed adaptations and important limitations for innovations in water supply and distribution that can improve urban resilience to wildfire. The manuscript details opportunities for new technologies and monitoring, with references provided for important recent research. The manuscript also links these options to literature on water distribution systems planning and modeling, which has considered disaster resilience but, to date, we know of no modeling done to evaluate resilience options for water distribution systems specific to wildfire. Finally, the manuscript discusses financial, management, and equity considerations, while also highlighting the important role of land use planning.

Improved leak localization in water distribution systems using SGTCC: Comparative analysis with EMD-HT and EEMD-HT under transient conditions

Use of continuous remote sensor water temperature monitoring to rationalize provision of clinical handwash basins in an ICU with water safety issues.

The impact of chlorine-based disinfection on the corrosion behavior of cast iron pipes in water distribution systems

Multi-objective optimization framework for prioritizing lead service line replacement in water distribution systems.

Pipe material significantly affected microbial regrowth and potential risks in reclaimed water distribution systems.

MILP model for optimal day-ahead scheduling of water distribution systems considering pumping efficiency and reliability

This study explores the potential connections between the digital economy and water conservation technologies in the context of China’s water resource consumption from 2008 to 2021. The research employs a state-of-the-art M-MQR technique, including the PCA index, and yields several significant findings. Empirical results reveal that digital technologies play a crucial role in reducing water consumption: Mobile technology decreases water use by −0.00001 to −0.00002 across quantiles, while internet access cuts consumption by −0.0000306 at lower quantiles and −0.0000167 at higher quantiles. The digital economy index shows an overall reduction in water consumption of −0.0537 at lower quantiles and −0.0292 at higher quantiles. Water conservation technologies, such as sprinkler irrigation, also contribute significantly, with reductions of −0.005 at the 10th quantile. Furthermore, water-saving investments show a positive effect on reducing water consumption, with reductions of −0.0105 at the 95th quantile. The study emphasizes that digitalization moderates the impact of water-saving technologies, reducing consumption by −0.0124 to −0.0118 at lower quantiles and −0.00812 to −0.00761 at middle quantiles. These results highlight the potential of digital infrastructure and water-saving investments to improve water use efficiency and address China’s water resource challenges. This study proposes that digital water supply and distribution system devices can help develop smart water infrastructure, reduce waste, and improve efficiency.

Small rural water utilities in the Appalachia region of the US often experience extreme water loss while struggling to maintain water quality compliance. This study quantifies the impact of reducing water loss on distribution system water quality in Martin County, Kentucky. Hydraulic and water quality models were developed, calibrated, and validated using EPANET for chlorine residuals and KYPIPE for trihalomethane (TTHM) formation. The models evaluated water loss reduction scenarios ranging from the current 70% to the industry target of 15%. Results showed that lowering water loss increased residence times, causing chlorine residual declines of 22–68%, with one site falling to the 0.2 mg/L threshold. TTHM concentrations increased by 12–18% in winter–spring and 26–44% in summer–fall, with two sites exceeding the individual 0.080 mg/L maximum contaminant level. These novel findings indicate that reducing water loss can unintentionally degrade water quality, underscoring the need for integrated planning. Recommended mitigation strategies include seasonal operational adjustments, water source and TTHM precursor management, optimized tank management, targeted flushing, and phased infrastructure upgrades. The modeling framework developed offers potential for broader application in other rural systems facing similar challenges.

Water supply networks are marred by serious risks of imperceptible pipeline leakage, posing ‎sustainability ‎and ‎performance ‎threats. This article highlights the use of vibratory signal features to get around the drawbacks of traditional methods in a highly detailed framework for leak detection based on CatBoost. demonstrated excellent diagnostic performance and carried out a thorough test performance evaluation on five leakage configurations . The expected system achieved an accuracy of 98.1% (variance (well within x/3% of expected):, beating traditional competitors such as Random Forest (97.3%) and Support Vector Machine (93.8%). ‎For example, the area under the receiver-operating characteristic curve was 0.995, indicating perfect or near perfect discrimination. Root mean square energy (32%) and spectral entropy (25) Indeed, their diagnostic characteristic characteristics were all in line with classic fluid dynamic laws Computational ‎‎efficacy allows real-time ‎system ‎deployment, with 0 .8 ‎milliseconds ‎per ‎every ‎classification ‎mandate ‎and ‎18-‎‎megabyte ‎memory occupancy. The specifications are actionable to create compatible configurations to enable follow-up and sustainable employment of infrastructure systems. By linking recent trends in machine learning to the practice of infrastructure monitoring, this study helps bring the world a step closer to achieving the SDGs.

With the rapid development of smart water distribution systems, real-time monitoring data from large-scale sensor networks plays a critical role in system optimization and failure prediction. However, sensor data quality is often compromised by faults and missing values, which significantly undermine the reliability of decision-making. To address this issue, this study proposes a spatiotemporal redundancy-based data recovery method for sensor data. Specifically, polynomial fitting and hierarchical clustering are employed to analyze the spatiotemporal redundancy inherent in sensor data, based on which a weighted feature matrix is constructed. This matrix is then subjected to dimensionality reduction to enhance data representativeness. Five models—Multivariate Polynomial Regression, Holt-Winters, Long Short-Term Memory Sequence-to-Sequence, Multi-scale Isometric Convolution Network, and Transformer—were systematically compared in data recovery tasks. Experiments were conducted using real-world data from a water distribution system in China, involving 58 pressure sensors and 36 flow sensors. Results demonstrated that the developed method achieved high accuracy alongside efficient computation, particularly excelling in scenarios with abundant spatial redundancy.