Locating Leaks in Water Distribution Networks Using Multi-Criteria Analysis

Water stands as the fundamental cornerstone of life, and with the human population growing exponentially, the critical importance of sustainable water management becomes increasingly evident. Water not only serves as the essential working fluid for major chemical processes, including thermal energy generation, but it is also integral to manufacturing and domestic use. Despite its significance, water is a scarce commodity, and the planet continues to lose this precious resource due to theft, pipe bursts, and leakages in water distribution networks (WDNs). The consequences of water loss extend beyond the impact on physical resources, as it is intricately linked to financial challenges. Thus, water loss has the potential to result in substantial financial setbacks for companies, organizations, and government entities, underscoring the interdependence of water management and fiscal sustainability. In response to these challenges, digital technologies have been harnessed to optimize WDNs. A major constraint lies in deciphering the applicable digital technologies due to the multidisciplinary nature of the research area encompassing WDNs and digital technologies; therefore, this study adopts a systematic literature review (SLR) combined with digital tools, to comprehensively extract all aspects of a WDN, ranging from sensors to enterprise resource planning (ERP), including equipment and systems. This literature forms the foundational basis for constructing a framework aimed at creating a content repository for digital WDN sustainability. To further develop the framework, this study reviews international best practices in digital WDN. This is adopted in developing a digital WDN framework based on the literature and classifying a database structure from sensor to ERP for a generic WDN. Furthermore, the study explores tools and systems for optimization as well as updating the theoretical structure with Johannesburg WDN data and provides a comprehensive evaluation for enabling digital WDN in South Africa. The digital WDN framework comprises three distinct components: security, a central management database (CMDB), and best practice architecture. The architecture involves five layers, including the physical layer, communication layer, data storage and analysis layer, benefit layer, and application layer. Utilizing systematic literature review and bibliometric keyword analysis, the study determines the equipment, systems, and their uses, leading to the development of a generic WDN. Validation of the framework using the generic WDN emphasizes the equipment and systems used in each of the five layers. Furthermore, the framework is validated using Johannesburg Water's WDN, leading to the development of a CMDB. The CMDB underscores the possibility of storing physical layer and communication layer configuration items (CIs) and highlights asset management, CI relations, change management, and historian capabilities. Maintaining an accurate and updated CMDB necessitates employing asset discovery solutions, while CMDB relationship mapping plays a crucial role in assessing the ripple effects of CI changes within a complex system. Finally, the measurement of the digital maturity of Johannesburg Water's WDN using the Administração e Gestão de Sistemas de Salubridade (AGS) maturity index reveals a maturity index of 62%. The index emphasizes strong capabilities in GIS integration and data collection through the SCADA system. Despite advancements in planning systems, such as domestic flow metering, there remains an opportunity to enhance asset management capabilities for continuous improvement in WDN operations.

Three-dimensional convolutional neural network for leak detection and localization in smart water distribution systems

Objective: This study aimed to implement a model for detecting and locating leaks in water distribution networks, using artificial intelligence (artificial neural networks). Background: Traditional leak detection methods, such as visual inspections and flow and pressure analysis, are being progressively complemented by solutions based on smart sensors and machine learning techniques. Method: Using the method of characteristics to solve the momentum and continuity equations, along with nodal equations, a hydraulic transient simulator was created for water distribution networks with leaks. For the leak simulation, the orifice law (Torricelli's Law) was used. Results and Discussion: The developed model successfully located the leak with an error of approximately 5.65%, corresponding to a distance of approximately 61.22 m for the studied network. Research Implications: Deteriorated infrastructure in water distribution systems can lead to leakage losses, reduced water transport capacity, component failures, increased maintenance and operating costs, frequent system interruptions, and decreased reliability. Thus, the exploration of rapid leak detection and location methods has increased, aiming to mitigate environmental impacts and the losses of natural and financial resources. Originality/Value: The methodological distinction lies in using pressure wave travel times as input data, rather than absolute pressure values. This approach eliminates the need for detailed hydraulic network calibration, reduces sensitivity to operational noise, and broadens the practical applicability of the model.

This study aims to propose Micro-electromechanical System (MEMS) accelerometers for leak localization in the water distribution network and assess the performance of machine learning models in accurately estimating leak locations. Intensive field experimentation was conducted to collect data for model development. Machine learning algorithms were employed to develop leak localization models, specifically artificial neural network (ANN) and support vector machine (SVM). Seventeen time-domain and frequency-domain features were extracted, and feature selection was performed using the backward elimination method. The results indicate that the ANN and SVM models are suitable classifiers for localizing leak distance. Both models achieved leak location predictions with over 80% accuracy, and the mean absolute errors were measured at 0.858 and 0.95 for the ANN and SVM models, respectively. The validation results demonstrated that the models maintained accuracies close to 80% when the distance between sensors and the leak was less than 15 m. However, the performance of the model deteriorates when leaks occur at distances greater than 15 m. This study demonstrates the applicability of MEMS accelerometers for leak localization in water distribution networks. The findings highlight the promising potential of employing MEMS accelerometers-based ANN and SVM models for accurate leak localization in urban networks, even under real-world, uncontrolled conditions. However, the current model exhibits limited performance in long-distance leak localization, requiring further research to address and resolve this issue.

The efficiency of water supply networks is an important issue. In order to reduce water losses, policies of leak reduction are essential. The paper deals with a preliminary study on the use of vibration monitoring tools for the detection of leaks in water service pipelines. The long-term project is the development of a system for automatically detecting burst leaks occurring in service pipes. Preliminary experimental tests were performed on both a test rig and an actual service pipe of the water distribution system. Three main objectives were achieved: firstly, the effectiveness of vibration monitoring for leak detection purposes was assessed providing a positive response; then, a prototypal detection procedure was studied, implemented and tested on the preliminary experimental data; finally, the specifications for a prototypal acquisition equipment were also determined. This paper illustrates the experimental campaign and its main results.KeywordsWater Supply NetworksMonitoringLeaksVibration

The detection and localization of leaks in water distribution networks (WDNs) is one of the major concerns of water utilities, due to the necessity of an efficient operation that satisfies the worldwide growing demand for water. There exists a wide range of methods, from equipment-based techniques that rely only on hardware devices to software-based methods that exploit models and algorithms as well. Model-based approaches provide an effective performance but rely on the availability of an hydraulic model of the WDN, while data-driven techniques only require measurements from the network operation but may produce less accurate results. This paper proposes two methodologies: a model-based approach that uses the hydraulic model of the network, as well as pressure and demand information; and a fully data-driven method based on graph interpolation and a new candidate selection criteria. Their complementary application was successfully applied to the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) 2020 challenge, and the achieved results are presented in this paper to demonstrate the suitability of the methods.

This chapter presents a data-driven based approach for detection of leaks in water distribution networks in which the demand is formed by a known periodic pattern plus a stochastic variable. The leak detection method is based on an adaptation of the dynamic principal component analysis (DPCA), and it is assumed that only pressures at selected consumption nodes are measured. Since the variables of water distribution networks (WDNs), even in normal conditions, are nonstationary and time-correlated the data are preprocessed with a periodic transformation previous to the application of DPCA. The proposed approach is validated with the Hanoi network model. The performance is evaluated with three indexes: the leak detection rate, the false alarm rate, and the delay of the detection with respect to the leak’s occurrence time. All of them are satisfactory for diverse leaks’ scenarios, and the proposed approach presents an improvement in the leak detection rate of approximately \(70\%\) as compared with the traditional PCA and DPCA methods.

Water loss poses a significant problem for water utilities and has received a lot of attention. To fulfill the increasing global demand for water, water supply system operations must be streamlined, making leak detection and location crucial. Water utilities have developed a number of techniques over time for finding leaks in water distribution networks (WDNs). These methodologies range from simple visual inspection to the use of hardware systems and now software using models and algorithms. Data from flow or pressure measurements, which are required for the analysis of water networks, is becoming more readily available with the introduction of intelligent sensor devices. Along with the introduction of geographic information systems (GIS) and supervisory control and data acquisition (SCADA) in the water sector, the deployment of model-driven methodologies for leak detection and localization has found extensive use. This paper aims to provide a concise introductory reference for early researchers in the development of a model-based approach for leak detection in WDNs. Thus, a survey of model-based approaches is presented, along with current research trends and applications of model-driven methodologies for leak detection in water supply networks. Several model-driven approaches and research studies for each case are discussed. Some challenges and research gaps are also discussed.

Water loss through leaking pipes constitutes a major challenge to the operational service of water utilities. In recent years, increasing concern about the financial loss and environmental pollution caused by leaking pipes has been driving the development of efficient algorithms for detecting leakage in water piping networks. Water distribution networks (WDNs) are disperse in nature with numerous number of nodes and branches. Consequently, identifying the segment(s) of the network and the exact leaking pipelines connected to this segment(s) where higher background leakage outflow occurs is a challenging task. Background leakage concerns the outflow from small cracks or deteriorated joints. In addition, because they are diffuse flow, they are not characterised by quick pressure drop and are not detectable by measuring instruments. Consequently, they go unreported for a long period of time posing a threat to water loss volume. Most of the existing research focuses on the detection and localisation of burst type leakages which are characterised by a sudden pressure drop. In this work, an algorithm for detecting and estimating background leakage in water distribution networks is presented. The algorithm integrates a leakage model into a classical WDN hydraulic model for solving the network leakage flows. The applicability of the developed algorithm is demonstrated on two different water networks. The results of the tested networks are discussed and the solutions obtained show the benefits of the proposed algorithm. A noteworthy evidence is that the algorithm permits the detection of critical segments or pipes of the network experiencing higher leakage outflow and indicates the probable pipes of the network where pressure control can be performed. However, the possible position of pressure control elements along such critical pipes will be addressed in future work.

Leaks in water distribution networks are estimated to account for up to 30% of the total distributed water; moreover, the increasing demand and the skyrocketing energy cost have made leak localization and adoption ever more important to water utilities. Each leak scenario is run on a simulation model to compute the resulting values of pressure and flows over the whole network. The values recorded by the sensors are seen as features of one leak scenario and can be considered as the signature of the leak. The key distinguishing element in this paper is to consider the entire distribution of data, representing a leak as a probability distribution. In this representation, the similarity between leaks can be captured by the Wasserstein distance. This choice matches the physics of the system as follows: the equations modeling the generation of flow and pressure data are non-linear. The signatures obtained through the simulation of a set of leak scenarios are non-linearly clustered in the Wasserstein space using Wasserstein barycenters as centroids. As a new set of measurements arrives, its signature is associated with the cluster with the closest barycenter. The location of the simulated leaks belonging to that cluster are the possible locations of the observed leak. This new framework allows a richer representation of pressure and flow data embedding both the modeling and the computational modules in a space whose elements are discrete probability distribution endowed with the Wasserstein distance. Experiments on benchmark and real-world networks confirm the feasibility of the proposed approach.

Control and reduction of leakage in water distribution networks are very important due to limitation of drinking water resources. Water distribution networks are currently designed based on hourly peak consumption. Variation of the hourly water consumption causes the pressure variation and increase in water leakage. If household storage tanks are used in buildings, the water networks can be designed based on daily peak consumption, resulting in considerable reduction in the hourly flow variation. In the present study, the effect of domestic water storage tanks on the leakage level of water networks has been studied using water hydraulic simulation. The results showed that the use of household tanks can significantly reduce the leakage level for water networks under operation. For the studied networks, the average leakage reduction level were 31% and 67% for tanks on the roof and in the parking lot, respectively. The results also showed that the branched or looped plans of water network have no significant effect on the amount of leakage reduction. Results of this study can be used in developing new design methods for water distribution networks aiming pressure management and control of the leakage level.

Leakages in water distribution networks have caused considerable waste of water resources. Thus, this study proposes a novel method for hydraulically monitoring and identifying regions where leakages occur in near-real time. A large network is first divided into several identification regions. To exploit a strong constructive and discriminative power, sparse coding is used, thereby adaptively coding the information embedded in observed pressures efficiently and succinctly. And a linear classifier is trained to determine the most likely leakage regions. A benchmark case is presented in this study to demonstrate the effectiveness of the proposed method. Results indicate that the proposed method can identify leakage events with enhanced tolerance capability for measurement errors. The method is also partially effective for identifying two simultaneous leakages. Certain practical advice in balancing the number of sensors and regions is also discussed to enhance the application potential of this method.

Pressure control is one of the main techniques to control leakages in Water Distribution Networks (WDNs) and to prevent pipe damage, improving the delivery standards of a water supply systems. Pressure reducing stations (PRSs) equipped by either pressure reducing valves or motor driven regulating valves are commonly used to dissipate excess hydraulic head in WDNs. An integrated new technical solution with economic and system flexibility benefits is presented which replaces PRSs with pumps used as turbines (PATs). Optimal PAT performance is obtained by a Variable Operating Strategy (VOS), recently developed for the design of small hydropower plants on the basis of valve time operation, and net return determined by both energy production and savings through minimizing leakage. The literature values of both leakages costs and energy tariffs are used to develop a buisness plan model and evaluate the economic benefit of small hydropower plants equipped with PATs. The study shows that the hydropower installation produces interesting economic benefits, even in presence of small available power, that could encourage the leakage reduction even if water savings are not economically relevant, with consequent environmental benefits.

Detecting and mitigating leaks in water distribution networks are vital for water conservation. Machine-learning-based (ML) acoustic leak detection models were introduced as effective alternatives for leak management. However, ML model training requires sufficient labeled data, which hinders related development. To address this challenge, this study employed contrastive learning (CL) for leak detection using limited labeled signals. Experimental results indicate that flip-x and amplitude scaling are optimal combinations for contrastive learning. Besides, ablation and t-distributed stochastic neighbor embedding (t-SNE) results reveal that increasing the model depth does not always yield performance improvement, and five convolutional blocks are more suitable for the leak detection problem in this study. Comparison experiments demonstrate that contrastive learning outperforms supervised learning (SL) when trained with insufficient labeled data. The out-of-sample validation results indicate that the proposed leak detection model is robust and effective in unexplored pipelines. The proposed framework significantly advances ML-based leak detection research and supports sustainable water management practices.

The efficient distribution of water is a subject of major concern for water utilities and authorities [1]. While some leaks in water distribution networks (WDNs) are unavoidable, one of the main challenges in improving the efficiency of drinking water networks is to minimize leaks. Leaks can cause significant economic losses in fluid transportation and extra costs for the final consumer due to the waste of energy and chemicals in water treatment plants. Leaks may also damage infrastructure and cause third-party damage and health risks. In many WDNs, losses due to leakage are estimated to account up to 30% of the total amount of extracted water [2]; a very important issue in a world struggling to satisfy water demands of a growing population.