Public access to high-quality freshwater has accounted for successful development in the last century. With rapid urbanization, water distribution networks (WDNs) spread over cities to supply freshwater to residential, industrial, and commercial districts. Over time, design approaches for water distribution networks improved, and more efficient facilities were employed in complicated WDNs [1
]. The quality of the end-pipe product was also controlled more strictly by environmental departments. However, in the present century, monitoring of complicated water networks has become a serious concern as WDNs age. Thus, advanced monitoring techniques need to be developed to meet the contemporary challenges in WDN maintenance.
One of the most common problems in WDN maintenance is a burst event in pipelines [2
]. Pipeline bursts, which can be caused by aging, corrosion, and deterioration of facilities, lead to significant economic, social, and environmental costs. The burst causes negative effects on the conveying pipes and other instruments of WDN, and this is directly or indirectly associated with additional economic costs due to water loss, diagnosis and repair of the WDN, and interruption in the water supply [3
]. In the aspect of the social costs, a pipe rupture in a WDN results in potable water loss and disruptions of customer service [5
]. Moreover, corrosion in the surrounding soil, leakage to underground water resources, and risks to public health are environmental impacts of water leakage due to pipe burst [6
]. Thus, prevention of water loss caused by bursts in WDNs has become a critical challenge in urban management in the recent two decades [7
Rapid detection of bursts is a promising remedy to the problem because it reduces the above-mentioned costs [2
]. A burst can occur for several reasons, including poor pipe conditions, inappropriate operation of the system, extreme weather conditions, and extraordinary environmental pressure. Hence, detecting the exact time and location of the burst is a sophisticated problem involving complicated factors. Conventional burst detection and isolation techniques are often lengthy procedures. In the conventional methods, a candidate area of analysis is uncovered by digging after visual inspection. This conventional technique entails extra costs due to the associated ground digging, traffic jams, and water losses [4
]. Therefore, research has been recently devoted to developing faster and more accurate burst detection approaches to decrease these costs [8
Statistical computation techniques based on observation of transient changes in recorded data have been extensively implemented to detect bursts. The statistical techniques extract information from a system, monitor data variations, and continuously reflect dynamic conditions of the system [9
]. Misiunas et al. suggested pressure-based cumulative sum (CUSUM) control charts to detect burst occurrence. They showed that outliers of the CUSUM indicated that pressure drop was induced by burst occurrence [10
]. Palau et al. used a principal component analysis (PCA) to extract key information from flow data sets measured by an supervisory control and data acquisition (SCADA) system. Control charts driven by the PCA identified anomalous behavior in the extracted flow data [11
]. Loureiro et al. implemented a region-based outlier statistical process control method to detect abnormal parameters in flowrates [12
]. It should be noted that monitoring burst occurrence using only pressure or flowrate in the pipelines does not lead to rapid detection [13
]. In this way, a multi-objective optimization sectorization method was proposed that considered the hydraulics, water quality, and economic factors together [14
]. Romano et al. obtained significantly accurate results on leakage detection using statistical process control [15
]. While their univariate statistical technique was easy to apply, a single variable was not sufficient to interpret the complex problem [16
]. However, a multivariate monitoring system can be used to overcome the limitations of the univariate burst monitoring systems and to improve accuracy on burst detection [17
When a burst is detected with a monitoring system, the burst location should be identified to hinder water leakage prior to repair of the WDN. However, WDN-related detection methods mainly concentrate on finding optimal sensor placements to develop early warning systems (EWSs) and improve data quality [18
]. A Fisher Discriminant Analysis was applied to identify the leakage location by sensor measurements. The results were satisfactory in a case study. However, the burst monitoring system could not provide comprehensive guidelines to detect burst locations [17
]. Thus, burst monitoring systems should be developed to not only detect accurate burst occurrence time, but also to isolate the exact burst location. Sarrate et al. proposed sensor locations for leak detection using a structural model and a clustering technique [21
]. Costanzo et al. suggested a model calibration tool to monitor and isolate sensor’s location using multivariate data [22
]. While both temporal and local detection were considered in their approach, hydraulic fluctuations were restricted. Pressure and flowrate time series have diurnal patterns as a result of diurnal water consumption patterns in the WDN. Moreover, the daily patterns of pressure and flowrate obtained from nodes are similar because of the geometric location of the nodes and demands in a WDN. Due to these complex dynamic patterns in WDNs, a burst isolation system with optimal sensor location is necessary.
The aim of the present study was to detect burst occurrence, determine optimal sensor configuration, and isolate burst location in a comprehensive monitoring and maintenance system. Accordingly, multivariate statistical and analytical techniques were applied to detect burst occurrence using flowrate and pressure data simultaneously. The results of the multivariate monitoring system were compared to those obtained by conventional univariate monitoring systems. The novel multivariate monitoring system consisted of standardized exponential weighted moving average (EWMA) and PCA to overcome the weaknesses of the conventional monitoring systems. The optimal sensor configuration was detected using the PCA and a k-means clustering algorithm. A sensitivity analysis was conducted to consider hydraulic and diurnal characteristics of the WDN. Finally, the proposed monitoring system and optimal sensor locating were interlinked to isolate the burst location in the WDN. The comprehensive system was compared with conventional systems using burst scenarios for the simulated WDN.
The present paper consists of four major parts. First, we present a branched WDN simulation that verifies monitoring systems and optimizes sensor configurations. Second, a novel standardized EWMA-PCA monitoring system is detailed for burst detection in the WDN. Two conventional systems using CUSUM charts and standardized EWMA were employed to compare the performance of the novel methodology in 10 burst scenarios. Third, optimal sensor configuration using PCA, k-means approach, and sensitivity analysis is discussed. The results are then compared with those of other methods. Fourth, a burst identification approach that minimizes an objective function in a mathematical model is presented.
A novel burst monitoring, isolation, and sensor placement system was proposed for water distribution networks based on multivariate statistical and analytical techniques. In the proposed method, a hybrid standardized EWMA-PCA approach and PCA, k-means, and sensitivity analyses were employed for burst monitoring and sensor placement, respectively. The proposed system was validated in a simulated branched WDN in 10 scenarios, and its performance was compared to those of conventional monitoring systems. The proposed system efficiently improved burst monitoring regardless of noise pattern and isolated the burst location. The average monitoring performance considering the noise was 93%, and the isolation ratio improved by 10% compared to the conventional systems because both pressure and flowrate were considered in the multivariate monitoring system, and the hydraulic characteristics of the nodes were included in the approach. Furthermore, the system could monitor small burst events that were not detected by the conventional monitoring systems. The sensitivity analysis results showed that the proposed system was robust to diverse burst events. In addition, the proposed system used optimal pressure sensors that were more economic than the flow sensors. The installation ratio improved by 20%, while the optimal number of sensors decreased by 40%. Alongside the economic benefits, the proposed burst detection and isolation system is anticipated to detect and isolate burst events in WDNs much faster than conventional systems.
Complex real WDNs are required to validate the proposed burst detection and isolation monitoring system in industrial settings. Furthermore, more scenarios including burst occurrence along the pipes need to be mentioned to compare the performance of the system in detecting the burst by the nodes and far from them. For this, big datasets should be employed using open source data generated in complicated networks. A deep learning model can be applied to the proposed system to predict, interpret and analyze the big-data in the WDNs. The application of deep learning algorithms to the proposed system can enhance the performance of burst monitoring and isolation in the complex real WDNs. It is anticipated that the monitoring system can be improved by these changes in the future in terms of the system’s efficiency and economic-social-environmental costs.