Exploring Resilience of Community Water Networks through CPHS-Enabled Platform

Transcription

1 Exploring Resilience of Community Water Networks through CPHS-Enabled Platform Qing Han, Phu Nguyen, Ronald T. Eguchi, Kuo-Lin Hsu, Soroosh Sorooshian, Nalini Venkatasubramanian The Donald Bren School of Information and Computer Science, UC Irvine, CA, US The Henry Samueli School of Engineering, UC Irvine, CA, US ImageCat Inc., CA, US. ABSTRACT Pipe breakage is one of the most frequent types of failure of water networks that often causes community disruptions ranging from temporary interruptions in services to extended loss of businesses and relocation of residents. Pipe leaks or bursts can lead to changes in pressure heads and flow rates, however, these characteristics also rely on other factors, such as topography, demographics, and pipe properties. Thus no general model can e ectively monitor the behavior of the entire pipeline network. In this paper, we present a novel leakage detection platform to identify the broken pipelines in the shortest possible time. The platform is a Cyber-Physical-Human System (CPHS), integrating the hydraulic model where the expected behavior is simulated by the backend computation system, with the real-time sensing that collects data through deployed sensors. When a discrepancy is observed, the platform triggers the hydraulic model updated to iterate to a solution that is the best match with the observations, which then may involve human to partially monitor and intervene the platform in order to minimize the disruption to the system. The objectives include creating an e cient network profile for a given water network via o ine study and locating the broken pipeline via online profile query and computations, that is, creating a large library of di erent system performances based on a variety of leak or break scenarios. The system is evaluated by simulating the behavior of water networks using hydraulic model EPANET combined with communications and control. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 2X ACM X-XXXXX-XX-X/XX/XX...$ INTRODUCTION Urban water distribution systems are essential for sustaining the economic and social viability of a community [1]. Over the years, these critical infrastructures have become complex and vulnerable to natural, technological and manmade events. When human health and safety, and lives also, are at stake, it is important to quickly isolate the dangerous region such that the disturbance may not ripple or cascade into other infrastructures due to the interdependencies of the lifeline systems. For example, pipeline bursts may cause transportation network collapse and water loss often lead to additional energy expenditures for transporting water from natural resources to the end users [28]. Hence, a Cyber-Physical-Human System (CPHS) enabled platform presents new possibilities to study how community water infrastructure dynamics (e.g. varying demands, disruptions) impact lifeline service availabilities and how service level decisions impact infrastructure control. It is the key to enabling resilient and scalable water distribution networks. The platform aims to (a) prevent water service failures by identifying operational degradation, (b) improve speed and accuracy of damage estimation in large hazards (e.g. earthquakes, floods) and (c) improve service restoration times in the event of large hazards. In this paper, we focus on identifying pipe breakage failures in the shortest possible time using an integrated simulation-sensing framework that we are developing called AquaSCALE. Pipe breakage, one of the most frequent types of faults in water systems, represents a very high cost vulnerability and is associated with public health implications and wastage of a limited critical resources [15]. A leakage detection system is an essential component of community water systems this is not a trivial task since the damage to underground pipelines is often hidden. Most of the methods to determine leak events are based on the measurement of leak-related vibro-acoustic phenomena with the help of certain equipments whose e ciency largely depends on the operator skills and the availabil-

2 AquaSCALE PLATFORM WATER RELATED EMERGENCIES rescue/evacuation Service Layer Flood Prediction First Response - Improve Resilience - Pub/Sub Mechanism - What-if Analysis - Multi-phenomena Joint Effect - Cascades of Events - Damage Prevention BreZo Flood Modeling Evacuation Modeling Database Analyzer Monitor hurricane rainfall flood pipe leak pipe burst pipe aging - Data Collection - Pub/Sub Observe earthquake EPANET Simulator Hydraulic Data water distribution network - Representation - Translation - Synchronization - Integration Infrastructure Layer G-WADI Adaptor - Systems Adaptation - Pub/Sub InLET Adapt Observable Earthquake Damage Hydrometeorological Data Estimation Figure 1: An overview of water related emergencies and a prototype of the AquaSCALE platform being the core of emergency management. ity of the devices [15]. Other detection techniques such as fluid transient methods, statistical analysis and machine learning are limited by making numerous assumptions on the network topography. Although there has been substantial work on this problem, to the best of the authors knowledge, no previous work has detected leakages occurring in the real-scenario based water network in an efficient manner using a combination of CPS infrastructures and simulations. In this paper, we consider the following detection complexities: (a) The pressure and/or flow characteristics rely on a number of factors such as topography, demographics and pipe properties, and no general model can e ectively monitor the behavior of the entire pipeline network [17]; (b) Modeling errors (di erence between theoretical model and actual system behavior) and observational errors impact the accuracy of results; (c) Insufficient monitored data due to limited number of sensors available or limited accessibility within the network raises the difficulty of accurate detection. To manage such complexities, an executable computational platform is needed to incorporate both hydraulic models and real-time measurements, together with human supervision. Unlike traditional model-driven approach, rather than the automated control, the human-in-the-loop can help in parameter identification for tuning and adaptation as well as parameter setting. By instantiating the interventions in a logical platform and presenting to the decision maker a rapid assessment of the benefits, the proposed CPHS-enabled leakage detection system can o er faster responses and improved consequences. The contributions of this work are as follows, To the best of our knowledge, we are the first to design an CPHS-enabled platform for identifying leakage in large-scale real-scenario based water networks. Compared with current detection techniques, the novel platform allows us to view water workflows as a community wide CPHS system with multiple levels of observation and control, reducing online computing complexity via o ine whatif analysis and saving costs by involving hydraulic models for exploring solutions to problems in cyberspace before instantiating them into a physical infrastructure. The platform enables an early post-leakage detection for water supply systems by using hydraulic simulation modeling updated with actual post-event pressure or flow measurements to iterate to a solution where likely pipe break locations and severities that could be tied to the observations. A variety of leak or break scenarios of the realscenario water network validate the capability of the proposed platform where the leakage event can be identified in a fast and timely manner. The participation of civil engineers helps in parameter tuning by considering the characteristics of the given water network. In the rest of this paper, we describe our cyber-physicalhuman-system approach (Section 2) in comparison to related work. Section 3 introduces and formulates the leakage detection problem; we develop a multiphase algorithm for e ective and near real-time leak detection in Section 4. Our proposed approach is validated through extensive simulations in an extended version of EPANET (Section 5) through a range of scenarios. 2. OUR APPROACH AND RELATED WORK To truly understand the behaviors of water pipelines and improve the resilience of water networks, we argue that a middleware based approach is required to integrate existing functional modules and improve information integration. A middleware can embed a variety of loosely coupled pre-existing simulators based on structural reflection and metamodel concepts to study the joint e ect and cascading of multiple phenomena [9, 1].

3 2.1 A Middleware Approach to Water Resilience In our approach, we aim to design a middleware framework, AquaSCALE, to identify the most vulnerable spots in community water infrastructures, and determine regions where more instrumentation and management are required for sustainable and resilient operation of the entire system. Figure 1 shows that the platform allows us to model, simulate and explore water systems at two layers, a higher service layer and a lower built infrastructure layer, by executing a logical observe-analyze-adapt loop at its core. Input to the service layer is derived from observations gather from sensors and stored in the data management module. Analytics modules that subsume models developed by domain experts operate on the near real-time data to generate higher level awareness for specific application tasks, such as flood prediction and evacuation planning. The awareness then triggers corresponding logical adaptations within the simulator, e.g. actuation and control of water infrastructure elements, that again generates a new set of observations. The platform is designed as a workflow based system comprised of multiple modules. Scenario Generation Module: A user of the tool can start by designing a situation by choosing a geographic region, the entities elements of interest in that region and then using the model selection process to identify the impact of the hazard at a temporal and spatial scale of choice. IoT/ Sensor data acquisition module: This module enables acquisition and gathering of real-time filed information, identifying e ects of new information on an analysis begun already, and projecting effects with updates from the field on a simulation outcomes. Integrated simulation and modeling engine: The use of available deep-domain software allows us to rapid prototype the initial model of the platform. Decision support module: Human in the loop can help in parameter identification for tuning and adaptation as well as parameter setting. 2.2 Related Work There are several main categories of research topics related to our work: sensor technologies, hydraulic modeling, and leakage detection techniques. Observation collection is the first stage of infrastructure failure detections, and requires smart sensing and e cient sensor placements. [11] designed and implemented a low-cost failure sensor for pipeline monitoring. In [2, 12], authors aimed to optimize the placement of sensors in municipal water networks for the detection of maliciously injected contaminants. These works formulated the placement issue as an optimization problem such that it can be solved in polynomial time. Various hydraulic models have been proposed for water distribution systems. EPANET is a promising hydraulic simulator, developed by the Environmental Protection Agency [23]. There are a number of EPANET based extensions, such as EPANET Z with geographic information [26] and WaterNetGen with pressure demand [18]. EPANET simulates hydraulics and water quality in pressurized pipe networks consisting of pipes, junctions (pipe joins), pumps, valves, and water tanks or reservoirs [1]. EPANET also provides several ways to model pipe leaks. In [2], leakage was simulated by adding a flow demand at the prespecified fractured pipe indicating the leak location since leak events involve water outflows. [21, 16, 19] modeled the leakage by Q = C A p (1) where Q is discharge flow rate, C is discharge coe cient, A is leak area (C A is known as the e ective leak area EC), p is pressure and is pressure exponent. The greater e ective leak coe cient EC is, the more severity of the leak event, and EC indicates the leak size that needed to identify by the platform. The default value of is.5 and its value typically varies between.5 and 2.8. To identify the hotspots of the leakage areas, [25, 7] have studied optimal replacement and expansion solutions for pipeline network. In [8, 14], future pipe failures were predicted based on the historical data and optimal rehabilitation strategies were planed in advance before the actual burst occurred. Many studies have focused on the specific techniques that can be used during a leak event. The primary approach for leak detection and location is acoustic inspection methods where an acoustic signal produced by the leak is monitored and detected [22, 6]. Since a sudden pipe leak often causes a pressure decrease which is followed by a transient wave traveling upstream and downstream along the pipeline, several fluid transient modelings were presented in [5, 13]. Machine learning techniques have been suggested for learning in leakage identification problems, such as a maximum likelihood method [2, 24], SCEM-UA algorithm [21] and neural network [4, 16]. A comparison of existing leakage detection technologies will be illustrated in Section 3. Our proposed CPHS-enabled platform AquaSCALE, incorporates sensing technologies, hydraulic simulator and computational component to enable communities to prepare to safely and e ciently respond to leak hazards, and it will be extended to a general framework to exploring resilience of water system in near future.

4 3. PROBLEM FORMULATION FOR LEAK DETECTION The goal of the leakage detection platform is to identify potential broken pipelines in a water network and assign them corresponding confidence intervals given the existence of the leak. Typically, a pipeline with a higher probability of breakage is more likely to be damaged. Based on the current state-of-the art, leakage can be detected by correlating changes in pipeline pressure and/or flow characteristics to changes in a hydraulic model for a given water network. This correlation has been achieved by updating a hydraulic model numerous times to find a scenario whose outcomes best match the monitored data obtained from the sensors [2]. This methodology was applied to a simple and small-scale network, a rectangular grid network with 5 pipe sections (the horizontal and vertical pipelines have same length) and 31 junction nodes (each with same elevation and flow demand). An exhaustive search on this graph is computationally expensive or even prohibitive for large-scale networks. A detailed comparison of the existing leakage detection methodologies is illustrated in Table 1 where the key advantages and limitations are listed. To ensure that our design is executable in real-world settings, we consider a canonical water distribution network without assumptions on its characteristics. Due to the dynamics of water systems, no single mathematical equation or neural network can e ectively monitor the behavior of the entire system. We, therefore, build an integration platform to allow the system simulation modeling to be updated with actual post-event observations. In order to reduce online computational complexity, we create a profile for a given network to allow di erent types of information processing on the same data at di erent levels of abstraction. The profile is a large library of di erent network performances based on a variety of leak or break scenarios, specifically, here it includes a sub-zone map divided by the deployed sensors. We begin by introducing the concept of a critical sensor. A critical sensor is defined as a sensor where the pressure measurement significantly deviates from normal characteristics in a hydraulic model. By identifying the critical sensor in di erent leakage scenarios simulations, we can divide a water network into several zones - this is later used in profile generation. The critical sensor can be used to indicate a damaged region for the sub-zone level identification. 3.1 A Two Phase Strategy for Leak Detection At the top abstraction level, level 1 (sub-zone level), a critical sensor can detect leakage within a zone based on the network profile; at this level, the system is unable to specify the exact position of the leak. In the next level of abstraction, level 2 (pipeline level), a Bayesian probabilistic network will monitor one or more pipelines within target sub-zones identified by level 1 and is able to locate potential broken pipelines. This paper focuses on single leakage detection for the preliminary study. In the future, multiple leakages will be further discussed by isolating them one by one since it is unlikely that several pipelines at random locations are broken simultaneously. An e cient methodology for single leakage detection is indispensable to future study. Though a particular leakage behavior (leak location and size, and its impact on pressure and flow characteristics) is unknown at system design time, relevant statistics can be obtained via extracting possible leakage behavior patterns from running extensive simulations with randomly allocated leak location and size. An e cient network profile can characterize the water system with various leakage behaviors, decomposing a given network into a number of sub-zones. Therefore, based on the network profile, the potential damaged candidates can be narrowed down to one or several sub-zones by identifying the critical sensor, and then pipeline level identification can be applied to the target sub-zones. Figure 2 shows a water network provided by EPANET. This is a real-scenario based map consisting of pipes, junctions (i.e. pipe joints), pumps, valves, storage tanks, and reservoirs. It includes geographic information such that the elevations of pipelines vary with the topography. Each junction has its own pattern of time variation of the demand (i.e. consumption) and each pipeline has di erent properties (i.e. length, diameter, roughness coe cient, and if it is open or close). Since the interconnect points are more risky than others [1], sensors are deployed at sporadic junctions of pipelines to roughly cover the entire network. Note that the optimization of sensor placement is beyond the scope of the paper. Sensors are labeled by associated location ID. For example, sensor s 11 is deployed at location 11. L i,j represents the pipeline connecting junction i and j, which is introduced for later convenience. By integrating sensor technologies into a hydraulic model, a series of observed data can be collected and compared with corresponding data from a hydraulic model with the goal of identifying a critical sensor that can best capture the leak. Consider the set of sensors S = {s i : s i is the sensor placed at location i} in Fig. 2 where S is the number of sensors, and assume that the sensor can monitor and report pressure values of associated locations at a certain frequency. The Pressure value is the principal data from which to provide features for leakage detection [17]. P (s i,t k ) for s i 2 S is the pressure heads at the monitoring locations i at time slot t k 2 T (T = {t k : k 2 [m, n], k 2 Z, m is starting time index, n is ending time index}) computed by hydraulic

5 Table 1: Comparison of existing leakage detection methodologies. Techniques Support Advantages Limitations Hear-and-Repair Sounding devices. Simple; high accuracy. Inverse Transient Analysis Probabilistic Model Machine Learning Pressure and flow sensors. Pressure and flow sensors; hydraulic simulator. Pressure and flow sensors; hydraulic simulator. Limited number of sensors required. Simple; limited number of sensors required. Consider di erent pipe properties, demands, elevations etc. Expensive; contamination; single pipeline. Deterministic; complicate; small-scale simple network; precision depends on sample frequency. Time complexity; small-scale simple network; precision depends on sample frequency. Extensive historical or simulated data required; o -line learning required. Sensor Junction S11 Pipeline with normal flow direction S263 S121 S115 S113 S267 Figure 2: A water network provided by EPANET with two reservoirs, a pump, a valve, a tank, 96 junctions, 118 pipelines, 12 sensors deployed at certain locations. model EPANET, while e P (s i,t k )representsthepressure measurements collected by sensor s i at time slot t k. 4. ALGORITHMS FOR LEAK DETECTION Identification of leakage event and determination of leak specifics should be fast since a longer discharge will waste limited water resources and increase the risk of contamination, infrastructure damage, and flood. To enable this, we discuss a profile-based two-phase identification technique to provide accurate advice in realtime. The o ine network profile generation is first introduced, and then the online two-phase identification algorithm is described, where Phase I is sub-zone level identification and Phase II is pipeline level identification. S183 S151 S129 S29 S255 S Offline Profile Generation Our first task is to create a profile of the water network. To create a profile, a large number of groups of P e (si,t k ) are simulated by running EPANET with stochastically allocated leak location and size. Since leakage causes water outflows, sensors deployed at different junctions may observe pressure values that are di erent from the model predicted values. For each group, a critical sensor can be identified by (2) argmax s i2s nx P e (s i,t k ) P (s i,t k ), (2) k=m where the outcome is a sensor that experiences the largest such deviation across all slots of observations. As shown in Figure 3, a partial network profile of the map in Fig. 2 indicates the sub-zone with its critical sensor. A sub-zone consists of a set of connected junctions and pipelines, where if there is a leakage, the most significant discrepancy will occur at its critical sensor. For example, junctions covered by a circle are those if there is leakage associated with one of them, the critical sensor of these cases will be s 11. It is worth noting that although di erent leak sizes of a specific location may result in di erent critical sensors, it is not a frequent case for most of junctions with leak size range from 1 to 4. Hence, the profile reflects the most likely pattern for single leakage behaviors. The approach is promising approximately 96% single leakage cases over 2 simulation runs satisfy the network profile, i.e. the critical sensor found by (2) is consistent with the one in the profile. For each simulation run, the location and size of the leakage is randomized in the map. 4.2 The Online Two-phase Identification Process The leakage identification procedure is divided into two phases i.e. a sub-zone level and pipeline level identifications. In Phase I, sub-zone level identification can quickly and accurately locate the damaged region

6 definitions of other notations are same as in Section 3.1. The threshold equals to 1.5% of the maximum value of f. Note that the accuracy of the identification increases with the threshold but at the cost of pipeline level complexity. Sensor Junctions belonging to SubZone S11 Junctions belonging to SubZone S263 Junctions belonging to SubZone S115 S11 Figure 3: Partial network profile of the map in Fig. 2. Sub-zones are indicated by associated critical sensors. based on the pressure measurements, producing a small number of leak events. Then, in Phase II, the hydraulic simulator is updated with observations of pressures to iterate to a solution where likely pipe break locations and severities that could be tied to observed pressures. The computational complexity depends on the number of updates of the hydraulic simulator but this number has been limited by Phase II Phase I : Sub-Zone Level Identification Sub-zone level detection aims to isolate damaged zones by identifying a critical sensor based on the network profile. The critical sensor is an indicative of the region and the severity of the damage. Given a sequence of observed data, a critical sensor can be computed by (2) and a sub-zone can then be located according to the profile. The accuracy of the identification depends on the e ciency of the profile. Even the network profile is able to provide an correct sub-zone with.96 probability, a 1% precision is highly desired because a failure of detection in sub-zone level makes pipeline level identification senseless. Therefore, a threshold is introduced by considering the cases where a few junctions with different leak sizes result in di erent critical sensors. Here, threshold equals to 1.5% of the maximum value of difference between observations and simulations, that is, if there are other sensors with similar discrepancy with the critical sensor, it will be considered as well. Hence, one or more sub-zones may be identified, and a set of target locations that will be examined in Phase II can be obtained based on the profile. In Algorithm 1, S is the original set of sensors and S rem is the set of remaining sensors, f(s i ) is to record the result associated with s i (s i 2 S), is the threshold to reduce the perturbation between two sensors, Z is the temporary solution set where the target sensors are stored, and R is the solution set of target locations obtained by querying the profile for each s i 2 Z. The S263 S115 Algorithm 1 Sub-Zone Level Identification. Initialize S rem = S; f(s i ) = for all s i 2 S, =1.5%, Z = ;, R = ;; P e (si,t k ) is collected and P (s i,t k ) is computed for s i 2 S, k 2 [m, n] (n m); for each sensor s i 2 S rem do f(s i )= P n k=m P e (s i,t k ) P (s i,t k ) ; end for s = argmax si2s rem f(s i ); Update Z = Z S s, S rem = S rem \ s ; for each sensor s i 2 S rem do if f(s i ) f(s ) apple f(s ) then Update Z = Z S s i, S rem = S rem \ s i ; end if end for for each sensor s i 2 Z do Update R by querying the network profile; end for Phase II : Pipeline Level Identification Given the potential damaged sub-zones indicated by sensors in R, a set of junctions will next be highlighted for further examination based on the network profile. A Bayesian network based algorithm can then enumerate certain possible leak locations combined with di erent leak sizes to locate a potential broken pipeline with the highest probability. It is worth noting that this enumeration is computationally practical due to the earlier step sub-zone level identification. The Bayesian identification methodology we use has been well developed and successfully applied in structural model updating applications, which allows for the explicit treatment of the uncertainties arising from modeling errors and measurement noise [3, 27, 2]. In this paper, the Bayesian methodology is coupled with EPANET for updating a parameterized class of hydraulic models. The parameters are chosen accordingly for simulating a set of possible leakage events (location and size of the leakage) for a given water system. The methodology can estimate the probability of each leakage event given the pressure measurements collected by the integrated sensor monitor, and the event with the highest probability is the most probable to occur. Let e =(e l,e s ) be the leakage event with leak location (e l ) and size (e s ) introduced in the parameterized class of hydraulic models, and ê =(ê l, ê s ) be the most probable leakage event. Note that e l and e s may be a vector if multiple leakages are considered. P e (s i,t k ) for s i 2 S

7 is the pressure heads at monitoring locations i at time slot t k computed from EPANET under event e. Given the pressure observations e P =[ e P (s i,t k )], a matrix of data obtained from sensors, the conditional probability that we maxmize is [2]: where Pr(e e P )=c [g(e)] g(e) = 1 S T ( S T 1)/2, (3) nx P e (s i,t k ) P e (si,t k ) 2. (4) k=m Thus, the most likely event ê =(ê l, ê s ) can be computed from (3) by tuning the combination of leak location and size. Since the paper focuses on single leakage scenarios, time complexity of obtaining ê depends on R. An efficient tuning criteria can quickly and accurately locate the broken pipeline. 5. VALIDATION In this section, we validate our proposed approach and platform for leak detection in water networks through a detailed simulation study. We begin by presenting the assumptions under which the simulations are conducted, and describe the simulation model including experimental strategy and performance metrics. The e ectiveness of the proposed leakage detection platform is evaluated by studying several pseudo scenarios for the water network shown in Fig.2. The network profile created and stored in Section 4.1 will be used in the following study. We assume the existence of leakage, thus we estimate the leakage event without the diagnosis of the occurrence of the damage. The pressure measurements are assumed to be accessed at certain locations where the sensors are deployed. 5.1 Experimental Strategy The leakage detection platform is established by integrating the EPANET hydraulic solver and wireless sensing with the information processing component where the sub-zone level and pipeline level identifications are implemented. Given an unknown leakage event, the pressure measurements can be collected by running EPANET with predefined leak location and size. The leakage event is simulated in EPANET by using (1). Here, =.5 and EC varies between 1 and 4 as the norm [23]. As mentioned in Section 3, t k is a time slot and one slot between t k and t k+1 depends on the sample frequency of sensors. Normally, pressure sensors log and report the pressure every 15 minutes [17]. The measurement error is simulated by applying a Gaussian random variable onto the observed pressures. Based on the network profile, we implement Algorithm 1 to identify the damaged region, and solve (3) over pipelines inside the target zones. To identify the exact broken pipeline, the information processing component of the CPHS system is composed of computing model as well as human interaction. The ability to incorporate a Human-in-the-loop allows civil engineers to interpret the results e ectively with hydraulic knowledge. Since both the position and the severity of the damage a ect the magnitude of changes in pressure characteristics, the leak location and size will be identified simultaneously but an accurate position is more crucial. The expected outcome of the system is a specific pipeline rather than precisely pinpointing the location of the leak on the chosen pipeline. This is reasonable because junctions adjacent to each other will be a ected in the same way and it is e ective to detect the pipe by relaxing the requirement of locating an exact position and instead capture two or more positions (two junctions indicate a pipeline). Normally, the actuation followed by the detection is to close an appropriate valve for decoupling the damage from the network, and in this case one or more pipelines will be shut down based on the deployment of valves. 5.2 Performance Metrics The e ectiveness of the proposed leakage detection system is evaluated in terms of two key performance metrics that are typical of precision/recall metrics in many data oriented systems: (1) Whether the pipeline associated with the leakage event can be located; (2) How many other unbroken pipelines are located along with the broken one. Metric (1) denotes straightforwardly the e ectiveness of the leakage detection platform, and (2) indicates the e ciency of the algorithm where unbroken pipelines may be located together with the broken one due to similar probabilities of leakage occurrence; note that fewer unbroken pipelines being located implies a better performance. The results of our experiments are obtained by studying several scenarios with randomly allocated leak events. 5.3 Scenario # 1 In this case, leakage event occurs on the pipeline L 11,13 with discharge coe cient 23 with perfect measurements available as shown in Fig.4. The pressure values are obtained by running EPANET with the ( unknown ) leakage event set to the corresponding assumed (i.e. true) values. Figure 5 shows that sub-zone level identification yields several potential dangerous locations that are further tested by pipeline level identification, which results in probability distributions of the occurrence of leakage over locations inside the target region. An obvious result here is that leakage at location 11 is the most likely event as the coe cient is equal to 2, and leakage at

8 Sensor Leakage Event Sensor Leakage Event Figure 4: Leakage event occurring on the pipeline L 11,13 with coe cient 23. Probability Leakage L(11,13) with coeff=23 (Sampling Once) 1 coeff=1 coeff=2.5 coeff=3 coeff= overall Junction ID Figure 5: Illustration of the detection of the damage occurring in Fig.4, showing the probability distributions of di erent coe cients (i.e. leak sizes) over several pipelines identified by Phase I with perfect pressure measurements at time slot t k. junction 13 for coe cient 3. It is consistent with the normalized accumulated probabilities (i.e. the overall performance shown in Fig.5), where the peak values appear at location 11 and 13. Thus pipeline L 11,13 can be located with the highest probability among the others, which is exactly in agreement with the predefined leakage event due to no other pipelines located. 5.4 Scenario # 2 In this case, leakage event occurs on the pipeline L 171,173 with discharge coe cient 23 with perfect pressure measurements accessible as shown in Fig. 6. According to Fig.7(a), it is di cult to identify the event, where several locations have similar and nonsignificant probabilities. It, however, shows that scenarios with coe cients equal to 2 and 3 have relatively obvious peak values, giving a new probability distribution shown in Fig.7(b). We can simply identify location 171 and 173 for coeff = 22, and location 35 for coeff = 25. Although location 35 has the highest probability based on the overall performance, we are unable to identify a pipeline associated with it since there is not another location showing an comparative prob- Figure 6: Leakage event occurring on the pipeline L 171,173 with coe cient 23. ability. Pipeline L 171,173 is then considered to be the most likely leak location, because location 171 and 173 are adjacent, and have similar and relatively high probabilities. Alternatively, instead of tuning coe cients, more pressure measurements provide several locations (171, 173, 161, 163, 164, and 166) with higher probabilities, as shown in Fig.7(c). Comparing Fig.7(c) with Fig.7(b), the interference terms, location 35, 161, 163, 164, and 166, can be eliminated, and pipeline L 171,173 can be identified since it can be noticed in both cases. 5.5 Scenario # 3 In this case, leakage event occurs on the pipeline L 211,213 with discharge coe cient 23 with measurement errors included as shown in Fig. 8. In Figure.9, based on the probability distribution of no error scenario, pipeline L 29,213 (L 29,211 +L 211,213 ) is located since location 29, 213 and 211 have relatively high and similar probabilities. In this case, the broken pipeline is identified since L 211,213 2{L 29,213 : L 29,213 = L 29,211 + L 211,213 }, but unbroken pipeline L 29,211 is located as well. In this instance, L 211,213 cannot be recognized due the high probability of location 29, though, it is good enough since L 29,211 and L 211,213 are adjacent and they will probably be shut down together to isolate the damage. As error is presented with var =.1, L 29,213 is identified. As var =.25, however, the detection result is L 29,211 for one set of observations of pressure (i.e. sample once), showing the broken pipeline fails to be located. Whereas L 211,213 can be identified for two sets of observations (i.e. sample twice), which is exactly consistent with the predefined leakage event. 6. CONCLUSIONS In this paper, we present the design and use of a novel CPHS-based platform to enable resilience in community water systems by addressing the problem of leakage management. The platform integrates hydraulic models with wireless sensor technologies, together with human

9 .2.1 Leakage L(171,173) with coeff=23 (Sampling Once) coeff=1 coeff=2 coeff=3 coeff=4 Sensor Leakage Event Probability overall Probability Junction ID (a) Leakage L(171,173) with coeff=23 (Sampling Once) 1 coeff=22 coeff=25.5 coeff= overall Figure 8: Leakage event occurring on the pipeline L 211,213 with coe cient 23. Probability Leakage L(211,213) with coeff=23 No error With error(var=.1) With error(var=.25) Sample Once Sample Twice Probability Junction ID (b) Leakage L(171,173) with coeff=23 (Sampling 6 Times).4 coeff=1 coeff=2.2 coeff=3 coeff= overall Junction ID Figure 7: Illustration of the detection of the damage occurring in Fig.6, showing the probability of possible leakage events in the target region. (a) shows the probability distributions of di erent coe cients with perfect pressure measurements at t k. The distribution cannot identify the broken pipeline and more specific coe - cients are evaluated, giving the new probability distributions shown in (b). (c) shows the predictions based on the perfect measurements at {t k+i : i 2 [, 5],i2 Z}. control, to allow di erent types of information processing on the same data at di erent levels of abstractions. From a system level perspective, this platform can pro- (c) Figure 9: Comparing the detection of the damage occurring in Fig.8 of two scenarios, without and with measurement errors, with simulated coe cient 2. var is the variance of the gaussian distribution, and the higher it is, the more error is introduced. As var =.25, two cases, sample once and twice, are compared. vide a tool for constructing, storing, updating and executing diverse water contexts and situations in order to study spatial and temporal aspects of water lifelines operating under normal and abnormal conditions, and to detect and predict their vulnerability to varying levels of shock intensity. In the future, we will enhance the problem formulations studied by considering multiple leakages, and extend the platform by integrating earthquake and flood simulators to explore the cascade of events. The ability to integrate diverse concurrently executing simulators in real world lifelines will enable us to understand the impact of failures and the interdependence of the multiple infrastructures and community processes. 7. REFERENCES [1] Community resilience planning guide for buildings and infrastructure systems. National Institute of Standards and Technology, 1, 215. [2] J. B. andw.e. Hart, C. Phillips, and J. Watson. A facility location approach to sensor placement optimization. Water Distribution Systems Analysis Symp., 247, 26.

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