Toward An Integrated Approach to LocalizingFailures in Community Water Networks

Qing Han∗, Phu Nguyen∗, Ronald T. Eguchi‡, Kuo-Lin Hsu∗, Nalini Venkatasubramanian∗∗ University of California, Irvine, ‡ ImageCat Inc., CA, US.

Abstract—We present a cyber-physical-human distributed computing

framework, AquaSCALE, for gathering, analyzing and localizinganomalous operations of increasingly failure-prone communitywater services. Today, detection of pipe breaks/leaks in waternetworks takes hours to days. AquaSCALE leverages dynamicdata from multiple information sources including IoT (Internetof Things) sensing data, geophysical data, human input, andsimulation/modeling engines to create a sensor-simulation-dataintegration platform that can accurately and quickly identify vul-nerable spots. We propose a two-phase workflow that begins withrobust simulation methods using a commercial grade hydraulicsimulator - EPANET, enhanced with the support for IoT sensorand pipe failure modelings. It generates a profile of anomalousevents using diverse plug-and-play machine learning techniques.The profile then incorporates with external observations (NOAAweather reports and twitter feeds) to rapidly and reliably isolatebroken water pipes. We evaluate the two-phase mechanism incanonical and real-world water networks under different failurescenarios. Our results indicate that the proposed approachwith offline learning and online inference can locate multiplesimultaneous pipe failures at fine level of granularity (individualpipeline level) with high level of accuracy with detection timereduced by orders of magnitude (from hours/days to minutes).

I. INTRODUCTION

Water is a critical resource and a lifeline service to com-munities worldwide; it is essential for sustaining the eco-nomic and social viability of a community [1]. Often theinfrastructures that capture, deliver and store water in citiesare many decades old. With the rise in urban populations,these infrastructures have become increasingly complex andvulnerable to failures due to natural, technological and man-made events. When human health and safety, and lives areat stake, it is important to quickly isolate faulty regions andprevent ripples into other interdependent infrastructures. Suchcascading impact results in community disruptions rangingfrom temporary interruptions in services to floods, extendedloss of business and mass relocation of residents.

Pipe leak is one of the most frequent types of failures incommunity water networks [2, 3]. Recent reports from LosAngeles Department of Water/Power (LADWP) and Wash-ington Suburban Sanitary Commission (WSSC) indicate thatcommunities are experiencing an unusual increase in pipebeaks, mainly in old pipes that are susceptible to corrosionproblems and pipe joint displacements caused by surfacedeformations. Extreme weather and heavy rainfall (e.g. Hur-ricane Sandy 2012, El Nino 2016, La Nina 2017) can stressalready weakened pipes to the point of causing major pipebreaks and significant increases in leak rates. Additionally,large-scale disasters can cause pipe failures that may drainvital water supplies required for extinguishing fires and other

hazards. Note that about 14-18% of water treated in the UnitedStates is wasted through damaged pipelines. Quality of watercan also be compromised via contaminant propagation througha faulty pipe. A large-scale pipe failures or a pipe burst maycause severe flooding. Those failures in water infrastructurecan have implications on other lifelines [4] - water loss oftenleads to additional energy expenditures for transporting waterfrom natural resources to end users; polluted water can createa serious public health danger; severe flooding can result intransportation network collapse.

Present status of instrumentation: Water is relativelyinexpensive resource. Consequently, most water networks aremetered only for billing purposes. In the absence of anymetering on water pipes, a utility can do little about leaklocalization except respond to customer complaints.

Unlike buildings or above-ground structures where damagecan be visibly seen, damage to underground pipes is oftenhidden. The only way to confirm break is to observe waterthat leaks to the surface. With the advent of sophisticatedmonitoring systems, such as SCADA (supervisory controland data acquisition) [5] and WaterBox [6], it is possibleto monitor pressure values and flow rates at key pointswithin the water network e.g. pump stations. However suchnetwork-level automatic control is too coarse-grained andcannot identify specific pipes that are suffering the effectof break. Instrumenting the entire system of pipelines withIoT sensors (pressure transducers and/or flow meters) is bothunfeasible (inaccessibility of locations) and expensive. Alsocommunity water systems are typically densely connected andcomplex networks with highly correlated measurements. It istherefore non-trivial to isolate anomalies accurately even witha complete observation.

Related localization approaches: One current practice is touse acoustic instruments listening for variation in the reflectedsignal, yet their effectiveness is only valid within an areaaround the leak and doing this is expensive [7]. Anotherapproach adopted by utilities is to use a calibrated hydraulicsimulator to localize the leak by enumerating possible leakypoints for a best match between the simulation result and theinlet and outlet meter data [8]. Although this appears plausibleand is also proposed in [9–11], it is computationally expensiveor prohibitive for single/multi-leak localization in large-scalewater networks. Because the position and severity of a leakjointly affect the hydraulic behavior, making it difficult toenumerate a match. Alternative methods studied in [12–15]are based on fluid transient modelings, since a sudden breakoften causes a pressure change followed by a transient wavetraveling along the pipe. However, the feasibility of this tech-

nique is complex due to the difficulty of obtaining a reliabletransient model for a pipeline network (rather than a singlepipe evaluated in the previous work). Several other techniquesusing current-flow centrality based approach [16, 17], stateestimation [18] or machine learning (ML) based techniques[19–24] have also been investigated. The performance of thesetechniques, however, are limited by specific contexts (e.g.single leak, a complete observation of the network, very smalland simple network topology).

Our study addresses a more realistic case where the avail-able measurement is limited by the type and number ofsensors and the objective is to localize multiple concurrentleaks (instead of single failure) of a real-world water network(instead of simple topology) in seconds/minutes (instead ofhours/days). To capture the dynamics of complex water net-work, we introduce AquaSCALE, a computational frameworkenabling the fusion of multiple different data sources, robustsimulation engines and plug-and-play ML techniques. To thebest of our knowledge, AquaSCALE is the first cyber-physical-human system (CPHS) enabled platform that (a) models com-munity water distribution infrastructures and pipe breaks/leaks,and (b) supports the integration of various information foridentifying multiple pipe failures.

Contributions of this paper:•Design and development of a CPHS enabled computationalframework to integrate multiple data sources and techniquesfor localizing leaks in community water networks - (Sec. II).•A novel two-phase process for leak identification using anoffline profile generation for quickly identifying potentialfaulty pipes and online live data integration for accuratelylocalizing damaged pipelines - (Sec. III/IV).•A plug-and-play analytic engine that enables selec-tion/integration of statistical ML techniques for fault identifica-tion and transforms low level pipeline information into higherlevel impact (e.g. floods) - (Sec. IV).•Extensive evaluations of the proposed approach under diversefailure scenarios using real-world water network - (Sec. V).•A prototype implementation of AquaSCALE that integratesmultiple sources of information - (Sec. VI).

II. APPROACH AND SYSTEM OVERVIEW

To quickly identify leak events in real-world water net-works, we argue that an integrated approach to fusing multiple(incomplete) sources of information is necessary. AquaSCALEis designed as a CPHS system - the architecture aims to inte-grate multiple technologies and information sources for local-izing leak events. IoT sensing data from water infrastructurescan track variations in the network and determine pipe breaksbased on reduction in pressure heads and increase in flow ratesat failure points [9]. The installation of IoT devices is timeconsuming and expensive; furthermore their measurements aresubject to uncertainty due to sensing errors and measurementcorrelations. To abstract out correct information with limitedIoT observations in a timely manner, sophisticated and highperformance algorithms are required. External conditions canbe used as additional information for failure detection. For

example, extremely cold temperature is likely to cause pipebreaks due to ice blockage - this knowledge can be used tocapture patterns of changes in pressure heads (increasing firstdue to pipe freeze and decreasing due to pipe leak). Humanleak-related reports can provide deterministic information. Theaggregation of external observations can help improve ourassessment of leak events.

As shown in Fig. 1, the core of AquaSCALE framework is adata-driven simulation engine that executes a logical observe-analyze-adapt loop. The input to the analyzer is derived fromObservations gathered from diverse data sources, and stored inthe data management module. The Analytics module subsumesmodels and techniques developed by domain experts and oper-ates on live data to generate higher level awareness for specificapplication tasks (e.g. leak detection and flood prediction).The awareness then triggers corresponding logical Adaptationswithin the framework (e.g. visualization tools for decisionsupport, actuation and control of water infrastructures). Torealize this observe-analyze-adapt loop, AquaSCALE is de-signed as a workflow based system comprised of multiplemodules described in Sec. VI.

In the paper, we apply AquaSCALE for pipe leak identifi-cation. AquaSCALE supports a novel two-phase approach formanaging water workflows at multiple levels of observationand control. In the first phase, statistical approaches are usedto drive the offline creation of a profile model of faults andtheir impact to help rapid identification of the problem innear real-time. While this initial phase significantly reducesthe online detection time, the second phase exploits the avail-ability of dynamic data and compensates for the limitationof the offline model to improve accuracy and efficiency. Tosupport a flexible suite of methods for leak events detection,AquaSCALE incorporates a plug-and-play analytic engine thatenables the selection/integration of statistical techniques forimproved identification of faults. Statistical based data inte-gration algorithms are used to incorporate IoT measurementswith additional observations. This analytic engine facilitatesthe discovery of an efficient composition of techniques forfailure localization in a given water network. In our prototype,robust simulations using an enhanced version of a commercialgrade hydraulic simulator EPANET (with added support forIoT sensors and failure modelings) are used offline to train aprofile model of anomalous events. The profile and multipleinformation sources are then used for online rapid coarsefault isolation and fine-grained fault localization (i.e. leakdetection).

III. MODELING RESILIENCE IN WATER INFRASTRUCTURE

Pipe leaks or breaks, as one of the most frequent typesof failures, represent a very high cost vulnerability and isassociated with public health implications and wastage oflimited resources. It is often caused by operation degradationof pipelines, extremely cold temperature, and large-scale dis-asters (e.g. earthquake). Leak events may be identified throughdiverse information sources - an unexpected reduction onpressure heads; an abnormal increase in flow rates; leak-related

- Data Acquisition

- Data Exchange

- Data Management

Monitor - What-if Analysis

- Simulation Engine

- Policy Engine (Rule)

- Machine Learning

Analyzer - Failure Detection

- State Estimation

- Community Notification

- Simulated Control

Adaptor

Network

Topology

Internal Measurement

Hydrometeorological

Observation

Human Input &

Report

Seismic

Report

External Observation

Pressure

Head

Flow

Rate

Observe Adapt

AquaSCALE Framework

Data-

base

Plug &

Play

Fig. 1: AquaSCALE prototype architecture. AquaSCALE enables the identification of anomalous events at two layers, a higherservice layer that determines water service availability and a lower layer that determines built infrastructure availability, viainformation integration and plug-and-play capability.

messages posted on social media platforms. Thus we introducemultiple information sources into AquaSCALE, and evaluateits efficacy in the treatment of pipe leaks.

This section introduces the modeling of leak event, IoTmeasurement in water infrastructures, weather information,and human input. Our experience indicates that IoT measure-ment alone may work well to identify a single leak event,but, as we explain later in Sec. III-A, it is not sufficientlyaccurate to isolate multiple concurrent failures. The combina-tion of diverse information sources provides new possibilitiesfor enabling detection of multiple pipe failures. In the realworld, extremely low temperatures can cause ice forming ina pipe that leads to complete ice blockage, and continuedfreezing and expansion inside the pipe increase water pressureheads that leads to pipe breaks. Thus ambient temperature,though is coarse-grained (city-level information), can providean additional pattern of pressure changing for leak localization.The damage to underground infrastructures is often hidden,and most pipe failures are silent until noticed by people.In the case where IoT measurement is unavailable, humanreports on leak events provide indispensable information. Suchweather temperature and human input when integrated withIoT measurement can help improve the detection outcome witha higher accuracy in a shorter amount of time.

A. Modeling Leak Events

A water system is represented as an undirected graphG(V, E) (water can flow in both directions) with vertices Vthat represent nodes (the joint of pipes), and edges E thatrepresent pipelines. | · | denotes the cardinality of a set. Theleak event is denoted as e = {e}, where an event e = (l, s, t)is identified by location e.l, size e.s, and starting time slote.t. The goal is to locate e.l for ∀e ∈ e. We use andenhance EPANET with the support for IoT devices and failuremodelings, named EPANET++. In EPANET++, pipe failuresare simulated by emitter that is device associated with nodeto model the flow through a nozzle or orifice that dischargesto the atmosphere [25]. Leakage continuously increases withpressure, and it is often computed using (1) in civil engineeringdomain [26–28]. More detail refer to [29]. The pipe leak ismodeled by

Q = EC · pβ (1)

where Q is discharge flow rate at the leak point, EC iseffective leak area depending on the discharge coefficient andleak area, p is current pressure head at the leaky node and βis pressure exponent. β typically varies between 0.5 and 2.5depending on the leak type, and we set it to 0.5 for generalpurpose [29]. EC indicates the leak size, i.e. e.s, and thegreater EC the more severity of a leak event. In single leakcontext, a node will be assigned as an emitter with a EC anda time stamp where the node is leak location (e.l), EC isleak size (e.s), and time stamp is leak starting point (e.t). Inmulti-leak case, one or more nodes will be assigned as emitterswith different EC but same time stamp, to simulate multipleconcurrent leaks.

Compared with single leak identification, multiple pipefailures become much more complex to detect and locate.By executing EPANET++, our empirical results show that thechanges on pressure head and flow rate are easy to be capturedin single failure case (Fig. 2). In scenario 1 where there isa single leak, the total change on pressure values of nodesin a certain distance range of e1 decreases with increasingdistance to e1.l (Fig. 2b - Scenario 1), and similarly for flowrates that is not shown in the paper for saving space. Herethe distance refers to the shortest path between two nodes,and the distance between two adjacent nodes is the lengthof the connection pipeline. This is because a sudden pipeleak often causes a pressure decrease and a flow rate increasewhich is followed by a transient wave traveling along the pipe[12, 30]. This pattern can be learnt and captured to identifya leak event. However it is hard to follow a certain changingpattern when multiple failures occur simultaneously, as shownin Scenarios 2 and 3. Multiple leak events interact with eachother and jointly affect the hydraulic behavior, resulting ina set of highly correlated observations that makes it difficultto extract correct message in a timely manner. In this case,external data sources and a hybrid of ML based techniques areused to compensate for the limitation of individual informationand improve the localization performance. It is worth notingthat multiple failures refer to multiple concurrent leak eventswhere the interval between the occurrence of any two events is

Scenario 1

e2

Scenario 2

Scenario 3

e1

e3 e4

e1

e1

(a) Scenarios

0

1

2

Scenario 1

2

4

6

Ch

an

ge o

n P

ressu

re H

ead

(p

si)

Scenario 2

0 0.9 1.8 2.7 3.6 4.5

Distance to e1.l (km)

0

5

Scenario 3

(b) Changes on pressure head

Fig. 2: Failure scenarios with corresponding changes onpressure head. (a) Three failure scenarios with single leakevent e = {e1}; two events e = {e1, e2}; three eventse = {e1, e3, e4}. (b) The sum of changes on pressure heads ofnodes within a certain range of the location of e1 along withincreasing distance to e1.l for each scenario.

less than the sampling frequency of IoT devices. The problemthen cannot be reduced to single failure detection because leakevents cannot be separated by time series.

B. Modeling IoT Measurements

The variation in pressure heads and flow rates due to pipeleaks can be used to obtain critical information on which partsof the system are suffering from the effects of water leaks.To model IoT measurements, a set of pressure and/or flowrate sensors A are simulated using EPANET++, where A ⊆V ∪ E since pressure head is measured on node while flowrate is measured on pipeline. The hydraulic time step, timeinterval between re-computation of system hydraulics, is usedto simulate the sampling frequency of IoT devices. The IoTobservations are filtered out based on the pre-defined sensorset A from the computed results of all nodes and links duringthe simulation time period.

We consider X as a set of IoT measurements collected fromsensors, and Y as a set of event variables, i.e. the states ofeach node (leak or not) that we wish to identify. An arbitraryassignment to X is denoted by a vector x = {xa : a ∈ A}.Similarly for Y , an assignment y = {yv : v ∈ V} isa vector of labels taking from the label set L = {0, 1}where yv = 1 indicates a leakage at node v. Note that theleak event is assumed to occur at node (the joint of pipes),since the interconnect points are more risky than others [1].In our implementation, leak locations are arbitrarily assignedmeaning that the structure of labels is independent, thereforethe conditional probability p(yv|x) can be modeled and trainedby using supervised ML based techniques [31]. This is amulti-output classification problem since the dimension of theoutput is more than one. Due to the mutual independenceof labels, the problem is then transformed to multiple binaryclassifications where a binary classifier is trained for each nodeindependently [32]. The goal is to maximize the number ofcorrectly classified labels by learning a set of classifiers that

30-40 40-50 50-60 60-70 70-80 80-90

Ambient Temperature (F)

0

2

4

6

8

10

Ave

rag

e #

of

leak

s p

er d

ay

Fig. 3: Average number of pipe breaks per day along withambient temperatures in the regions of Prince George’s andMontgomery County’s for recent five years (2012-2016).

maps x→ y, which is

yv = arg maxyv∈y

fv∈V(x, yv) (2)

Each fv is a compatibility function indicating how well yv fitsthe input x, and yv is the prediction for x that maximizes thecompatibility. Given the knowledge of a water network, MLbased techniques can be used to find a solution to (2), howeverthe prediction capability may be limited by the uncertainty ofIoT measurements due to noise and interference, incompleteobservations due to inaccessible locations and high cost, andhighly overlapped observations due to tightly interconnectednetwork structures.

C. Modeling Weather Information

When the ambient temperature falls to 20 degrees F orbelow, pipes may be subject to freezing in the event ofextremely cold weather [33]. According to general manager’sreport from WSSC and weather report from National Oceanicand Atmospheric Administration (NOAA), pipes become morebrittle during the winter and the chance of water main breaksrises significantly as the temperature drops (Fig. 3). Coldweather can be a root cause of pipe breaks. The measurementbased study in [33] addresses that frozen pipe itself doesnot typically cause a break. Instead continued freezing andexpansion inside the pipe increase water pressure that candramatically increase stress on a pipe and cause the pipebreak following a pressure decrease. Therefore, the pattern ofa pressure increase followed by a decrease can help isolate afaulty pipe. That is if the ambient temperature is below 20◦F ,a time series of pressure values will be processed that mayprovide additional information for a faulty pipe. As mentioned,the paper is to evaluate the effect on leak localization byintegrating multiple information sources and techniques. Thusweather information is modeled straightforward using proba-bility representation. For each node v, we define pv(freeze) asthe probability of freezing if the temperature is below 20◦F ,and pv(leak|freeze) as the conditional probability of leak dueto freezing. Markov chain will be studied for the modeling ofweather information in the future.

D. Modeling Human Inputs

To leverage human inputs, we bring in social media, OnlineSocial Network (OSN), for the incorporation of human sens-

ing. OSN has become a major platform for information sharingin which we can mine interested patterns. Human reports onpipe leak events, such as leak messages posted on Twitter,can help identify the potential damaged region by extractingthe associated geographic information. Compared with IoTmeasurement, human input is considered as deterministicinformation because it is highly likely to have pipe breaksin a region if people living around report it on OSN. Themore reports the higher the level of confidence on the event.Therefore, we incorporate human inputs with the predictedoutcome from IoT measurements to improve the detectionperformance.

Data collected from Twitter represents a previously un-tapped resource for detecting a pipe break and locating thefailure. Human input to AquaSCALE is enabled by integratinga novel Tweet Acquisition System (TAS) [34] developed atUC Irvine, which enhances the monitoring of tweets based onclient/application needs in an online adaptive manner such thatthe quality and quantity of results improve over time. Givena group of interested patterns, TAS can extract related tweetsthat are then used to help track and locate leak. Twitter usersare “sensors” and the posted message with a mention of waterpipe break such as “Pipe bursts @ Sunset Boulevard north ofthe UCLA campus.” is an indicator of leak event. To modelthe human inputs, let C = {c : c = {v : |lc−lv| < γ∧v ∈ V}}represent a set of subsets of V (i.e. a set of cliques) inferredfrom tweets. Here, a clique c is associated with the locationlc where people post the event, and |lc − lv| < γ means thatthe distance between lv (the location of node v) and lc is lessthan threshold γ. The threshold γ is a pre-defined parameterindicating the coarseness of the collected Twitter data. Forexample, if γ is set to 1 km, nodes within 1 km distance tolc are considered to be likely to leak and will be added intothe clique c.

Although there is a high probability for a region to havea pipe break if leak message posted on Twitter, a tweet canbe erroneously treated as an indicator of a leak event. Forexample, tweets like this “LeakFinderST - innovative leakdetection and location in water pipes.” may be collected butit does not relate to a leak event that we wish to identify.Thus we define a probability of false positive error as pe, i.e.the likelihood of a tweet that is improperly considered to berelevant, where 0 < pe < 1. The confidence that there is aleak within a certain region is represented by

pt = 1− (pe)k (3)

where k is the number of tweets collected over a periodof time, and with more tweets collected the confidence ptincreases. To model the number of messages received alongwith the time, we use Poisson distribution that is popular formodeling the number of times an event occurs in an intervalof time or space. The human input is assumed to arriveindependently of the time. The average number of humanreports received in a sampling interval (of IoT devices) isdesignated as λ that is called arrival rate. The probability ofreceiving k reports in n elapsed time slots is given by theequation

P (k reports in n intervals) =(nλ)ke−(nλ)

k!(4)

where e is the Euler’s number and n ∈ N. Combine (3) and(4), the confidence that there is a pipe leak in an area can becomputed.

IV. A COMPOSITE LEAK IDENTIFICATION ALGORITHM

To enable an accurate and timely leak events identifica-tion, we discuss a two-phase approach where the profilemodel is generated offline by learning an extensive amountof measurements in water infrastructures (Phase I) and theadditional observations are integrated with the predicted resultsfrom the profile model when live data coming in (Phase II).The proposed composite algorithm reduces the detection timeby orders of magnitude by generating a profile offline andimprove the detection accuracy by incorporating multiple datasources.

A. Phase I: Training Profile Model Using Measurements InWater Infrastructures

In Phase I, the objective is to train a set of classifiers fv∈V in(2) to generate a robust profile model f using a great amount ofmeasurements collected in water infrastructures. Here we dropthe subscript v and use f to represent a set of trained fv∈V asthe profile model. We first discuss the generation of trainingfeatures and samples that are then input into the classifiers fora profile model generation. The enabling of plug and play MLbased techniques allows us to explore the knowledge of whichtechnologies work well in terms of speed and accuracy underdifferent configurations.

Features of internal measurements in water infrastructuresinclude the topology of the network and IoT observations. Thebasic topology information, denoted as T , includes node ele-vation, pipe length, diameter and roughness coefficient, whichare static parameters for a given water network. Dynamic IoTmeasurements X collected from IoT sensors depend on thetype and location of the devices. Techniques based on themeasurements from pressure and flow rate devices allow amore effective and less costly search in situ [35]. Thus we usepressure transducers and flow meters in the paper. Water pipeleak identification is based on the premise that leakage in oneor more locations of the network involves local liquid outflowat leaky points, which will change the pressure head and flowrate at monitoring points [35]. Therefore, we use the differencebetween two sets of consecutive readings from IoT devices asthe features of X . That is xa is the change on pressure heador flow rate of sensor a. The dynamic IoT observations Xaggregated with the static topology T are then the features of atraining sample. AquaSCALE in conjunction with EPANET++

enables the selection of a sensor set A giving the type andnumber of IoT devices. It allows the study of sensor placementby evaluating different sensor configurations. The problem ofidentifying an optimal sensor placement for leak detection willbe studied in future work. In this paper, given the numberof available devices, we use k-medoids algorithm to select agroup of locations as the sensor set. K-medoids is a clusteringalgorithm related to k-means, but it is more robust to noise

and outliers [36, 37]. k-medoids partitions |V|+ |E| potentialsensor locations into certain number of clusters and assignscluster centers as the sensor locations, based on the pressurehead and flow rate read from nodes and pipes.

As discussed in Section III-B, this multi-output classifica-tion problem is transformed to multiple binary classificationproblems where the classifier is trained separately for eachpotential leak location v using same datasets and its true labelsdenoted as Yv . The profile model f : T ∪X → R can be anensemble of a set of linear/nonlinear predictors, decision trees,or weak learners, and the parameters of f can be learnt byML based techniques on the basis of the analysis of pressureand flow rate variations produced by the leak. Note that theperformance of specific techniques depends on the structure ofwater networks, the type and number of IoT devices and theirdeployment. AquaSCALE allows to test different techniques inisolation or combination, and a hybrid approach may improvethe performance since it is thought of as a way to compensatethe limitations of individual algorithms.

In the paper, we used scikit-learn package for data process-ing and analysis [38], and compared multiple well-known MLalgorithms including Linear Regression (LinearR), LogisticRegression (LogisticR), Gradient Boosting (GB), RandomForest (RF) and Support Vector Machine (SVM). We proposeda hybrid approach named HybridRSL, a combination of RFand SVM via LogisticR, because RF and SVM remain robustwith decreasing number of IoT sensors, and LogisticR has lowvariances and is less prone to overfitting. As shown in Fig. 4,the same dataset is trained and predicted by RF and SVMseparately, and their predicted results, i.e. leak probabilitiesfor each node, are then aggregated as a new feature set andinput into LogisticR for further learning. Algorithm 1 showshow classifiers are trained and updated to generate the profilemodel for Phase II.

Fig. 4: A sketch of the workflow of HybridRSL approach.

Algorithm 1 Training the Profile Model

1: Input water network topology T , IoT measurements X ,true leak events Yv and classifiers fv for v ∈ V

2: Output the profile model f = {fv : v ∈ V}3: Objective update fv to best fit training samples

4: for v in V dofv.fit(T,X, Yv)

5: end for

B. Phase II: Inferring Leak Locations Using Live Ingress Data

In Phase II, we sequentially aggregate multiple data sourcesto infer the leak locations. Compared with human inputs, IoT

measurements and ambient temperatures are relatively stabledata sources. We can expect telemetry readings from thesetwo sources at a certain interval once the sensing devicesare deployed. Due to dynamic and complex social behavior,however, human reports on leak event maybe not available.Therefore, we first use IoT and temperature streams for eventprediction, and use additional human inputs for event tuning.

The live IoT observations x = {xa : a ∈ A} togetherwith the topology information T are firstly learnt by theprofile model f . It uses predict proba and predict methodsbuilt in the scikit-learn package, whose outcomes are thescore/probability of leak for each node, i.e. P = {pv(i) =score(yv = i) : 0 6 pv(i) 6 1 ∧

∑i pv(i) = 1 ∧ i =

{0, 1} ∧ ∀v ∈ V} with yv = 1 indicating having a leakat node v, and a subset of V that are predicted to leak,i.e. S = {v : pv(1) > pv(0) ∧ v ∈ V}. If the ambienttemperature is below 20◦F and a location v is detected tobe frozen, its predicted leak probability pv(1) will aggregatewith pv(leak|freeze) based on Bayes’ theorem [39]. This is awell-known method to combine probability distributions fromexperts in risk analysis, and to apply it into AquaSCALE, wesimply consider each information source as an expert. Theupdated leak probability at node v is

p∗v(1) =q∗v(1)

1 + q∗v(1)(5)

where

q∗v(1) =

n∏j=1

g1j(pj |qv = 1)

g0j(pj |qv = 0)(6)

q∗v(1) is the posterior odds of the occurrence of leakage atnode v; g1j (g0j) represents the probability of source j givingprobability pj conditional on the occurrence (non-occurrence)of leakage at node v. Here, the predicted probabilities comefrom two information sources, IoT measurements and weatherdata. In this manner (5), more sources of information meansmore certainty. For example, if the probability of leak is 0.6predicted by both two sources, then p∗v(1) will tend to bemuch higher than 0.6. The aggregated results then updatedP and S correspondingly. In set S, potential faulty pipesare identified. However ML based techniques with noisy IoTsensing data work on the predictive perspective whose outputis probabilistic. We use entropy to measure the uncertainty ofa predicted event (leak or not) at node v on the basis of itsleak probability, which is defined as

H(yv) = −1∑i=0

pv(i) log pv(i) (7)

The corresponding uncertainty function is given by

E[y] =∑v∈V

H(yv) (8)

In order to minimize (8), AquaSCALE integrates additionalhuman input to help to enhance the knowledge of leaks andincrease the determinacy of the predicted events.

Human reports on leak events as deterministic informationare able to correctly reflect pipe failures within a certainregion, but are unable to specify an exact damaged positiondue to various social behaviors. Therefore, the human inputis used as an additional subzone-level information, working

with the pipeline-level outcomes P and S, to enforce theevent consistency and improve the prediction results. The eventconsistency here refers to the consistency of the pipeline-leveland subzone-level predictions. An inconsistent event meansthat none of pipes in the subzone identified by human inputsis currently predicted to leak. To leverage the human inputs,we apply the higher order potential concepts used in theimage segmentation problems, which is used to enforce labelconsistency in image regions [40]. We define a higher orderpotential function Φc : L|c| → R on clique c to assign acost to each possible configurations (or labelings) of y. Byincorporating human inputs, (8) can now be written as

E[y] =∑v∈V

H(yv) +∑c∈C

Φc (9)

that is the energy function to be minimized. Because the effectsof human inputs is considered to be non-negative in the paper,Φc can assign a very high cost to clique c if none of nodesin c is currently predicted to leak, i.e. @v ∈ S for ∀v ∈ c.In this case, the node in clique c with the highest entropy(uncertainty) will be selected for further processing.

The higher order potential used by us can be written as

Φc =

0 if ∃v ∈ S for v ∈ c0 else if H(yv) < Γ for ∀v ∈ cInf else

(10)

Here we introduce a threshold Γ to decide if a pipeline-levelprediction is considered to be determinate enough to ignoringthe subzone-level information. That is if the entropy for node vis less than the threshold Γ meaning that the current predictedevent is likely to occur, then the leak information on node vwill not be updated by human inputs. According to (9) and(10), an inconsistent event can push the energy to the infinity.In order to minimize (9), in Algorithm 2, a set of leak locationsS is firstly identified by the profile model f and then updatedbased on clique c by adding a candidate v∗ if Φc = Inf andv∗ = arg maxv∈cH(yv). Correspondingly, pv∗(0) and pv∗(1)will be updated to 0 and 1, and H(yv∗) will be 0. In thismanner, the inconsistent events will be forced to change andthe total energy will be reduced because the infinite potentialsare eliminated and the entropy of certain nodes are reduced.

V. EXPERIMENTAL STUDY - USING AQUASCALE FORLEAK EVENT IDENTIFICATION

In this section, we evaluated the proposed identificationapproach on single- and multi-failure scenarios, tested mul-tiple ML based techniques in isolation and combination, andexamined the impact of incorporating IoT measurements andadditional observations. We begin by describing the setupand datasets under which the experiments are conducted, andintroduce the performance metrics and the results.

A. Experimental Setup and Datasets Generation

Water Networks. AquaSCALE is evaluated using twowater networks - a canonical water network provided by theEPANET (named EPA-NET) and a real subzone of WSSC wa-ter service area provided by WSSC (named WSSC-SUBNET).

Algorithm 2 Inferring Leak Events

1: Input water network topology T , IoT measurementsx, profile model f , leak probability due to frozenpv∈V(leak|freeze) and human inputs C

2: Output an updated set of leak locations S3: Objective minimize E[y] in (9)

4: /* Event Prediction */

5: P = f.predict proba(T,x); S = f.predict(T,x)6: for v in V do7: if v is detected to be frozen then8: q∗v(1) = pv(1)

pv(0)∗ pv(leak|freeze)

1−pv(leak|freeze)

9: pv(1) =q∗v(1)

1+q∗v(1)

10: pv(0) = 1− pv(1)11: S = S ∪ {v} if pv(1) > pv(0)12: end if13: end for

14: /* Event Tuning */

15: C = {c : c = {v : |lc − lv| < γ ∧ v ∈ V}}; Φc∈C = Inf16: for c in C do17: if ∃v ∈ S for ∀v ∈ c then18: Φc = 0, break19: end if20: if Φc 6= 0 then21: v∗ = arg maxv∈cH(yv)22: if H(yv∗) > Γ then23: pv∗(1) = 1, pv∗(0) = 0, S = S ∪ {v∗}24: end if25: end if26: end for

A graph representation of EPA-NET with |V| = 96 and|E| = 118, and WSSC-SUBNET with |V| = 299 and|E| = 316 is shown in Figure 5. The elevation of pipesvaries with the topography, and each pipe has four attributes- length, diameter, roughness coefficient, and status (open orclose controlled by a valve). Each node has a pattern of timevariation of the demand (i.e. consumption), and leak events aresimulated at nodes. EPANET++ is used to perform extendedperiod simulation of hydraulic behavior, which computes pres-sure heads at nodes and flow rates at pipes.

IoT Sensing Data. Extensive simulations are run on thesetwo networks using EPANET++. Given the number of devices,we first identify a set of sensor locations, and generate agreat amount of IoT measurements for profile training. Asmentioned, features of a training sample are the topologyof a water network and changes on sensing values. Thenumber of training and testing samples are 20, 000 and 2, 000respectively. For each simulation run, there is at least oneand at most 5 leak events, and the number of events followsthe uniform distribution i.e. U (1, 5). The leak events aregenerated with arbitrary locations and sizes but same startingtime since we aim to study concurrent failures that are harder

EPA-NET WSSC-

SUBNET

Fig. 5: A graph representation of EPA-NET - a canonical waternetwork provided by EPANET with 96 nodes, 118 pipes, 2pumps, one valve, 3 tanks and 2 water sources, and WSSC-SUBNET - a subzone of WSSC service area with 299 nodes,316 pipes, 2 valves and one water source.

to pinpoint. The sensor set A is selected using k-medoidsalgorithm based on the given information of IoT devices. Thesampling frequency of IoT devices is 15 minutes. Since thegoal is to identify leak locations, we assume that the leakstarting time e.t is known. The change on pressure heads andflow rates is then computed by taking the differences betweenthe sensing values at e.t−1 and e.t+n, where n is the numberof elapsed time slots after leaking, as in (4).

Human Sensing Data. From January 6, 2016 to April1, 2016, the east coast of the US experienced extremelycold temperatures, while the west coast experienced highprecipitation due to El Nino effects. We collected 30 million“leak-related” tweets posted in the US during this period usingTAS system. Since this data contains significant noise, it wastreated as described (Sec. III-D). Based on the result statistics,the arrival rate λ of human inputs is set to 1 per 15 minutes,and the false positive error pe is set to 0.3. The coarsenessparameter γ, determining the clique c, is set to different valuesto test the impact of incorporating with human inputs. More in-depth analysis of those tweets, such as the distribution amongdifferent facilities and how soon after the leaks are the tweetposted, will be discussed in future work. In Algorithm 2,the node in c with the highest entropy will be consideredas the most risky point, and it will be predicted to leak ifthe entropy is greater than threshold Γ. Here Γ is set to 0 toalways consider human effect. For each simulation run, givenan elapsed time slot n, a random number between 0 and 1is generated for obtaining the number of received tweets kbased on (4), and the confidence probability pt can then becomputed based on (3). With the lapsed of time, more humanreports can be collected to help identify pipe failures.

Environmental information - Ambient Temperature. Inthe paper, the probability pv(freeze) and pv(leak|freeze) areset to 0.8 and 0.9 respectively for all v ∈ V . It mightbe different for every node since the vulnerability to lowtemperature depends on a variety of factors, e.g. material,age, location, which will be studied in future work. For eachsimulation run, a random number between 0 and 1 is generatedfor each node and it will be used to decide if the connected

pipe is frozen based on the pre-defined probabilities. It is likelyto have more pipe failures under extreme cold temperatures,which will be used to drive failure scenarios.

Failure Scenarios. We evaluate the proposed composite al-gorithm of pipe leak identification through two-failure scenar-ios over different evaluation strategies. We generated 20, 000single- and multi-failure scenarios separately for training and2, 000 for each for testing. Single Pipe Failure representsthat there is only one leak event, which is denoted as e ={l, s, t}. While multiple pipe failures represents that multipleleak events occur simultaneously, denoted as a set of eventse = {ei : i = 1, ...,m} where m is the number of leakypoints and ei = {li, si, ti}. Multi-failure is often caused bythe ice blockage in winter, thus Pipe Failures due to LowTemperature is considered as the use case of multiple leaks.The faulty pipes will be located by using different strategies -measurements in water infrastructures with diverse ML basedtechniques, weather information, and/or human inputs.

B. Performance Metrics

The effectiveness of the proposed algorithm is evaluatedin terms of following metrics. Hamming Score is defined as∑v∈V

1[yv=1∧yv=1]1[yv=1∨yv=1] where 1 is an indicator function. It is the

number of leak events correctly predicted divided by the unionof predicted and true leak events. The score is bounded by 1and the higher the score the greater number of leaks that areidentified. Percentage of IoT Observations is the percentageof IoT deployment penetration. In practice, we want to reducethe number of devices since the installation and maintenanceare very expensive. HereA = V∪E with |A| = |V|+|E| refersto the full (100%) IoT observations. Elapsed Time Slot is thenumber of time slots elapsing after the leak event, denoted asn. A time slot is a 15 minutes time interval, determined by thesampling frequency of IoT device. With n increasing, on oneside, more observations including IoT data and human inputwill be collected, which may provide more information. Onthe other side, it may also waste more water, and increase therisk to public health due to water contamination and to otherinfrastructures due to cascading events.

C. Experimental Results

In this section, the proposed approach for leak event identi-fications is validated through a detailed simulation study. Webegin by plugging and playing several ML based techniquesfor both single- and multi-failure scenarios using EPA-NETnetwork, and apply Hybrid-RSL technique that outperformsothers for following experiments. The effectiveness of integrat-ing diverse data sources for failure detection is evaluated byrunning extensive simulations on both EPA-NET and WSSC-SUBNET networks. Flood as a cascading event is modeledand predicted to help explore the impact of pipe failures.

Figure 6 illustrates the comparison of different ML basedtechniques for single leak identification. Those techniqueshave similar high hamming scores as using 100% IoT ob-servations (Fig. 6a), while RF and SVM can keep a betterperformance even with 10% IoT (Fig. 6b). Figure 7a/7b show

the comparison of RF, SVM and Hybrid-RSL in terms ofhamming score for single- and multi-failure scenarios. With alower percentage of IoT observations, RF yields a higher scorecompared with SVM. With more IoT data available, SVMoutperforms RF as using around 70% IoT in multi-failurescenarios. With the aggregation of RF and SVM, HybridRSLhas the best performance in both single- and multi-failurecases. It also shows that multi-failure is much harder to locate.Other ML and data fusion techniques can also be plugged andtested using AquaSCALE.

In the following result, HybridRSL with the highest scorewill be used, and the integration of multiple data sourceswill be examined. Here the distance threshold γ for humaninputs is set to 30 meters. Figure 8 describes how much doweather and human data together contribute toward identifyingMultiple Failures due to Low Temperature using real-worldWSSC-SUBNET. In Fig. 8a, only IoT data is applied, and theresult obtained by aggregating temperature and human input isshown in Fig. 8b. The plotted surface shows how the hammingscore varies with the percentage of IoT observations and theelapsed time slots. It clearly illustrates that AquaSCALE withthe integration of weather and human information is robust forlocating leak events even with limited IoT data. Incorporatingwith human input can increase the score, however, morehuman inputs as the time elapsing do not provide significantimprovement. Because the false positive error of human data issmall in the simulation. Figure 7c/8c present the increment onhamming score by incorporating weather and human data, andthe incrementation is more significant with less IoT data. Fig-ure 9 shows that the efficacy of incorporating with human inputdecreases with the coarser Twitter data. By adding temperatureinformation, however, it can compensate the impact of loosehuman data and keep the score higher. In Fig. 10, detectionusing only IoT data is sensitive to the maximum number ofleak events, but the aggregation of additional information canhelp locate failures and output a better result.

LinearRLogisticR GB RF SVM

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Fig. 6: EPA-NET with Single Failure - Comparison of MLtechniques for single leak identifications using (a) full and (b)10% IoT observations.

D. Exploring Impact - Flood modeling and prediction

To capture the impacts of pipe failures and improve post-event awareness, AquaSCALE incorporates flood modelingand prediction to study cascading events. We apply BreZo sim-ulator for flood prediction on WSSC-SUBNET water network.BreZo is a hydraulic model and has been successfully applied

in simulation of dam breaks [41, 42] and floods [43, 44]. Itcan efficiently simulate water flows in varying shapes of theearth surface. A detailed description can be seen in [45]. InBreZo, the flood is predicted based on the digital elevationmap (DEM), interpolated from node elevations, shown in Fig.11a. To feed leak information into the flood model, we use (1)to calculate the outflow rate based on pressure readings, whichis then input into BreZo for flood simulations. Two leak eventsare simulated at v1 and v2 with different leak sizes but samestart time, and Fig. 11b shows that the flood spreads along theearth’s surface. This information can be used by water agenciesand city planners for damage control, community notificationsand evacuation plans.

VI. TOWARDS A PROTOTYPE IMPLEMENTATION

AquaSCALE framework is a flexible and extensible plat-form to capture and visualize dynamic community water sys-tems at multiple levels. Our initial implementation of AquaS-CALE is designed as a workflow based system comprised ofmultiple components.

The Scenario Generation Module enables water managersand analysts to provide meaningful and diverse water contextsto the framework by generating a range of situations. Auser of the tool can start defining a situation by choosing ageographic region, entity elements of interest in that region,and using additional modules to identify hazard, vulnerability,restoration, and impact of the hazard at a temporal and spatialscales of choice. The Sensor Data Acquisition Moduleenables gathering of real-time field information for predefinedscenarios by projecting the effects of new updates from thefield on simulation outcomes. The Integrated Simulation andModeling Engine executes EPANET++ and BreZo to simulatethe dynamic behavior of water networks and interactionsbetween water infrastructures and floods. EPANET++ allowsus to model sensor devices and embed them at interested loca-tions, collect pressure heads and flow rates, capture hydraulicand water quality behavior, simulate single/multiple leaks andstudy impact of damage to infrastructure components. A Plugand Play Analytics Module is used to plug and unplugspecific information, such as data sets and algorithms, atwill depending on the specific context of applications, and tounderstand the advantage and limitation of diverse strategiesin isolation and combination. Users/operators/analysts interactwith AquaSCALE using the Decision Support Module tomanage devices at runtime as they identify vulnerable spotsand address accuracy/cost tradeoffs and, to optimize sensorplacement for a better performance.

VII. CONCLUDING REMARKS

In the paper, we introduced AquaSCALE, a CPHS compu-tational framework, and use it for localizing leaks in commu-nity water networks. We formulated multi-leak identificationproblem, developed an ML based integration mechanism forfusing information from multiple sources, and evaluated itusing real-world and synthetic networks. AquaSCALE canbe used by water agency operators with expertise in civil

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Fig. 7: A group of comparisons running on EPA-NET. Comparison of RF, SVM and HybridRSL in terms of hamming score for(a) single- and (b) multi-leak identifications. (c) Average increment on hamming score by adding weather and human inputs.

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Fig. 8: WSSC-SUBNET with Multiple Failures due to Low Temperature - Average hamming score for multi-leak identificationsusing (a) IoT and (b) multiple data sources, and (c) increment on hamming score by integrating weather and human data.

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High: 154

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Fig. 11: Flood prediction based on (a) DEMof WSSC-SUBNET with leaks at v1 and v2.(b) Zoom-in flooding map overlaying over theDEM. Flood flows from the center to the outer.H represents the flood depth in meter.

infrastructures to explore problems and solutions in cyberspacebefore instantiating them into a physical infrastructure. Forexample, a large section of water systems (usually an entirepressure zone) can be shutdown to prevent cascading failuresof pipe burst and to preserve critical water supplies. Suchexploration, proactive planning and their effective instantiationduring damage/shutdown is relevant in global contexts and is

a topic of future research.Acknowledgements: We are grateful to Prof. Sharad Mehrotra, Prof.Soorosh Sooroshian, Mehdi Sadri, Dan Hoffman, Lingyi Zhang forproviding input, tools and datasets for this work. This work wasfunded under NSF Awards No. CNS-1143705, CNS-0958520, andCNS-1450768.

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