Journal of Environmental Management 334 (2023) 117463

Contents lists available at ScienceDirect

Journal of Environmental Management
journal homepage: www.elsevier.com/locate/jenvman

Research article

Multi-objective optimization of hydrant flushing in a water distribution
system using a fast hybrid technique
Mostafa Shoorangiz a, Mohammad Reza Nikoo b, *, Jirka Šimůnek c, Amir H. Gandomi d, e,
Jan Franklin Adamowski f, Malik Al-Wardy ga

Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran
Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman
Department of Environmental Sciences, University of California, Riverside, USAdFaculty of Engineering & IT, University of Technology Sydney, Sydney, NSW, AustraliaeUniversity Research and Innovation Center (EKIK), Óbuda University, 1034, Budapest, HungaryfDepartment of Bioresource Engineering, McGill University, QC, CanadagDepartment of Soils, Water and Agricultural Engineering, Sultan Qaboos University, Muscat, Omanbc

A R T I C L E I N F O

A B S T R A C T

Keywords:
Hydrant flushing
Risk-based analysis
Runtime reduction
Contamination event grouping
Parallel water quality simulation

As a critical element in preserving the health of urban populations, water distribution systems (WDSs) must be
ready to implement emergency plans when catastrophic events such as contamination events occur. A risk-based
simulation-optimization framework (EPANET–NSGA-III) combined with a decision support model (GMCR) is
proposed in this study to determine optimal locations for contaminant flushing hydrants under an array ofpotentially hazardous scenarios. Risk-based analysis using Conditional Value-at-Risk (CVaR)-based objectives
can address uncertainties regarding the mode of WDS contamination, thereby providing a robust plan to mini­
mize the associated risks at a 95% confidence level. Conflict modeling by GMCR achieved an optimal compro­
mise solution within the Pareto front by identifying a final stable consensus among the decision-makers involved.
A novel hybrid contamination event grouping-parallel water quality simulation technique was incorporated into
the integrated model to reduce model runtime, the main deterrent in optimization-based methods. The nearly
80% reduction in model runtime made the proposed model a viable solution for online simulation-optimization
problems. The framework’s capacity to address real-world problems was evaluated for the WDS operating in
Lamerd, a city in Fars Province, Iran. Results showed that the proposed framework was capable of highlighting asingle flushing strategy, which not only optimally reduced risks associated with contamination events, but
provided acceptable coverage against such threats, flushing 35–61.3% of input contamination mass on average,
and reducing average time-to-return to normal conditions by 14.4–60.2%, while employing less than half of theinitial potential hydrants.

1. Introduction
Water distribution systems (WDSs) can be susceptible to accidental
hazards such as leakages from a municipal sewage network or otherunintentional sources — e.g., aging or poorly connected pipes and soil
subsidence (Hrudey and Hrudey, 2006; Kroll, 2006; Schwarzenbach
et al., 2010; Mansour-Rezaei, 2013). Contaminated water distributed via
WDSs in Japan (Yokoyama, 2007), Canada (Hrudey et al., 2003), Scot­
land (Gavriel et al., 1998), and America’s Elk River watershed (Whelton
et al., 2015) are examples of contaminant intrusion leading tosubstantial public health problems. Such events have gradually raised
the profile of WDS contamination issues. The threat of an intentional
injection of contaminants aimed at endangering public health must also
be addressed, particularly given the perceived heightened risk of such
events since 9/11 (States et al., 2003). Hence, there is an attendant needfor preparedness studies to deal with such emergencies (Berry Jonathan
et al., 2005). Completely restricting access to WDS reservoirs, tanks, andother elements is not an achievable proposition, nor is it a sufficient
strategy to guarantee a distribution system’s safety and security (Ostfeld
and Salomons, 2005). The current most reliable approach is to first* Corresponding author.
E-mail addresses: mosishoorangiz@gmail.com (M. Shoorangiz), m.reza@squ.edu.om (M.R. Nikoo), jiri.simunek@ucr.edu (J. Šimůnek), gandomi@uts.edu.au(A.H. Gandomi), jan.adamowski@mcgill.ca (J.F. Adamowski), mwardy@squ.edu.om (M. Al-Wardy).
https://doi.org/10.1016/j.jenvman.2023.117463
Received 12 September 2022; Received in revised form 31 January 2023; Accepted 3 February 2023
Available online 18 February 2023
0301-4797/© 2023 Elsevier Ltd. All rights reserved.

M. Shoorangiz et al.

Journal of Environmental Management 334 (2023) 117463

identify the presence of contamination in the network through efficientmonitoring, obtained by placing contamination detection sensors in
different segments of the WDS (Kessler et al., 1998; Guidorzi et al., 2009;
Chang et al., 2011; Zeng et al., 2016; Naserizade et al., 2018; Khorshidi
et al., 2018, 2019). A codified strategy must then be available to reduce
the associated risks following the detection of the contamination.
Various studies have been conducted on different operational responses
to contamination events — e.g., contamination containment, hydrant
flushing, and broadcasting an alert to consumers (Poulin et al., 2008,2010; Guidorzi et al., 2009; Rasekh and Brumbelow, 2015; Shafiee andZechman, 2012, 2014, 2017; Khaksar Fasaee et al., 2020), and on theidentification of source characteristics (Laird et al., 2005; Preis andOstfeld, 2006; Zechman and Ranjithan, 2009; Haxton and Uber, 2011;
Liu et al., 2011, 2012). However, given the simplifications undertaken to
reduce computational expense, such as considering only limited
contamination scenarios, as well as inefficiency in quick responses toreal-world WDS contamination events, these approaches lack sufficientreliability.
Although the removal of contaminants from a WDS by hydrant
flushing is believed to be the most cost-effective method to mitigate
adverse impacts (Friedman et al., 2002; U.S. Environmental Protection
Agency USEPA, Office of Pollution Prevention and Toxics, Risk Assess­ment Division, 2005; Poulin et al., 2008), it is not a straightforward
process, and challenges remain in its efficient planning. While a
coherent flushing strategy must be effective for a wide range of haz­ardous contamination events, in most previous studies, only a small
number of contamination events served as the basis for developing
flushing rules (Afshar and Najafi, 2013; Shafiee and Zechman, 2015;
Bashi-Azghadi et al., 2017; Moghaddam et al., 2020). In offering an
optimal flushing strategy as a function of an activated sensor, Khaksar
Fasaee et al. (2020) achieved notable progress by considering a rela­
tively wide range of contamination events. However, since convergenceto global optima requires evaluating numerous possible strategies in avast decision space for a large set of contamination events, theirapproach was only marginally applicable to large urban WDSs withmany consumer nodes and potential hydrants. Further, the slow andtime-consuming conventional simulation-optimization model compu­
tation approach used in their study made it impossible to achieve results
within a reasonable period of time. The demand-driven hydraulicsimulator was another drawback as it shows limited accuracy in deter­mining actual nodal demands and hydrant discharge rates, especially incases where the pressure drops sharply due to opening the hydrants andnominal demands cannot be satisfied.
To address these limitations, this study investigated a combination ofnovel approaches:
i. A hybrid contamination event grouping-parallel water quality
simulation method was introduced for the first time to solve thecomputational burden problem arising from the fact that each contam­
ination event must be simulated separately for each set of flushing hy­
drants. This is the case because the high discharge rate of hydrants
affects network hydraulics and the contaminant’s propagation regime.
ii. To cope with contamination intrusion uncertainties (e.g., node,time, and duration of injection), the present study employed Conditional
Value-at-Risk (CVaR) analysis to calculate the expected value of loss at a
certain level of confidence (Rockafellar and Uryasev, 2000, 2002).
Although this formulation has been used in some water resource man­agement studies (Yamout et al., 2007; Shao et al., 2011), its efficacy has
never been the subject of previous WDS research.
iii. Graph Model for Conflict Resolution (GMCR), a form of gametheory, served in the final decision-making framework to designate thestable optimal solution among non-dominated solutions found by theNon-dominated Sorting Genetic Algorithm-III (NSGA-III). Game theory
provides a collection of approaches for tackling conflicts (Hipel et al.,2020) and has served to model and analyze some water resource man­agement conflicts (Madani, 2010; Batabyal and Beladi, 2021), especiallythrough non-cooperative games (Yu et al., 2015; Taravatrooy et al.,2019; Dowlatabadi et al., 2020). GMCR demonstrates how an equilib­
rium state can be obtained in multi-objective—multi-decision-makerstudies (Nash, 1950, 1951).
In summary, the present study’s ultimate goal was to provide aframework for comprehensive hydrant flushing planning in response to
WDS contamination events that addressed the following:i. Online simulation-optimization models (i.e., optimization models
using a simulator to calculate objectives) tend to have a long runtime,
especially when uncertainties are being investigated with a large set of
possibilities. More specifically, common practices of integrating EPA­
NET and an optimization model have proven very time-consuming.
ii. As many uncertainties exist in how a contaminant enters a WDS,
the average performance of operational responses may not be a reliableindicator of the overall level of network security. Stochastic analysis oftarget objectives is likely to provide a better assessment.
iii. A comprehensive multi-objective investigation of operational
responses in a WDS is necessary to obtain a holistic image and infor­
mation about different aspects of the system. This way, the model can beused in real-world problems where multiple Decision-Makers (DMs),
each with different priorities, are involved.
2. Materials and methods
The flowchart of the proposed methodology for a comprehensive
contamination management model (Fig. 1) consists of six main steps.
Step 1 EPANET simulation model development
Hydraulic and water quality simulations were performed using
EPANET software and its MATLAB® toolkit (version 2.2, a dynamic link
library to link EPANET simulations with MATLAB®) (Rossman, 2000;
Eliades et al., 2017). An EPANET hydraulic and water quality simulation
model for the Lamerd WDS was developed with improvements uponearlier work to create reliable hydraulic and water quality simulations(Ghaedi, 2009; Naserizade et al., 2018; Khorshidi et al., 2018, 2019).
These improvements included changing demand-driven hydraulics topressure-driven to ensure accurate hydrant discharge, nodal demand,and correct hydraulic behavior and contaminant spread underpressure-deficient conditions. This model drew upon WDS physical data
(e.g., nodes, pipes, and their characteristics) and basic information (e.g.,
nodal base demands and hourly multipliers of the daily demand pattern)
for the simulation. Final model calibration was done to meet the daily
base water demand of approximately 260 L per capita, considering the
population assigned to each node (the total population is estimated atabout 81,000), and hourly multipliers of the daily water demand
pattern. More information about EPANET hydraulic and water quality
simulations is available in section S1 of the Supplementary Materials.
Step 2 Potential hazardous scenarios dataset gathering
The performance of flushing strategies should be evaluated using a
representative set of potential contamination events while accounting
for the uncertainties involved. In this study, this set was designed basedon the extent and duration of contamination propagation according to
the city’s layout and daily water demand pattern, considering points at
risk of contaminant intrusion. As illustrated in previous studies, zerobase-demand nodes have the greatest contamination spread whenselected as injection nodes due to their easy accessibility, connection to
main water pipes, and the relatively high volume of water passing
through them (Afshar and Miri Khombi, 2015; Naserizade et al., 2018).
Different times and durations of contaminant injection were selectedbased on daily water demand pattern and some probable durations of apotential incident. Finally, a sensitivity analysis was carried out to rank
the scenarios in terms of their contamination spread, with
no/low-impact events being omitted from further consideration.

2

M. Shoorangiz et al.

Journal of Environmental Management 334 (2023) 117463

Fig. 1. Flowchart of the proposed comprehensive contamination management model.

• Step 3 Optimal sensor placement

were simulated, and Water Quality Matrices (WQMs) were stored foroffline optimization of the sensor layout. Each event was assigned to thefirst sensor activated following the intrusion, dividing contamination
events into a number of clusters where the cluster number was the sameas the sensor number. The main objective was to minimize the averagetime-to-detect (i.e., the time gap between injection and detection) of all
scenarios, with each WDS node being considered a potential sensor node
- binary decision variable (1 means the node is selected as a sensor po­sition, and 0 means it is not).

Within a WDS, a well-conceived contamination detection sensor
layout is critical to achieving a robust contamination management
protocol. As efficient contamination flushing is highly dependent on theimmediate detection of the contaminant and a quick opening of hy­
drants (Rathi et al., 2015), sensors must be arrayed in such a manner as
to detect input contamination in the shortest possible time, with aminimum number of sensors detecting all contamination scenarios(given possible budget constraints). To do so, all contamination events3

M. Shoorangiz et al.

Journal of Environmental Management 334 (2023) 117463

Step 4 Preliminary contamination events screening

3.1. Pressure-driven hydraulic simulation

Since a frequency analysis of the values of objectives under uncer­
tainty was required to obtain their risk-based values, a preliminary
screening was necessary to detect and exclude scenarios in which
opening all potential hydrants would not bring about a meaningful
remedy. Otherwise, the analysis could not be properly carried out, andthe risk-based objective values would not be improved in the optimi­
zation process. To this end, contamination events in which opening all
potential hydrants an hour after sensor activation in demand-driven
mode resulted in either a flushed mass of less than 10% of the input
contaminant (for the first risk-based objective), or a reduction of lessthan 5% in WDS time-to-return to normal conditions compared to taking
no action (for the second risk-based objective), were identified andomitted.

For pressure-driven hydraulics, Wagner’s equation (Eq. (1)) was at
the core of interconnecting nodal actual demand to nodal availablepressure head (Wagner et al., 1988):⎧
Di pi ≥ Pf
⎪
⎪
⎪ (
⎪
)1/e
⎨
p − P0(1)
qDi = Di i
P0 < pi < Pf⎪
Pf − P0⎪⎪⎪⎩0 p i ≤ P0
where
qDi Actual demand at node i,pi Available pressure head at node i,
Di Full nominal demand of node i, met when pi equals or exceeds Pf,
P0 Pressure head below which the actual demand is 0, and
1/e Pressure function exponent set at 0.5.
To comply with the typical standard for the flow rate of public hy­
drants, Pf was set at 138 kPa (20 psi) and P0 at 0. Head loss in a virtual
link connecting node i to a virtual reservoir with fixed head P0 + Ei canbe expressed by inverting the power function portion of Wagner’s
equation (Eq. (2)) (Rossman et al., 2020):

Step 5 Hydrant flushing integrated risk-based simulation-optimization
model (RSOM) development
A coupled EPANET–NSGA-III simulation-optimization model wasdeveloped to identify each cluster’s optimal flushing strategies. Opti­
mization algorithm scripts and functions were written from scratch tohave a tailor-made optimizer for this study. EPANET-MATLAB®-Toolkit
was called in the objective function of the optimization algorithm, andthe values of optimization objectives were calculated and delivered tothe optimizer in each iteration. Zero-base-demand nodes were consid­
ered potential hydrants to flush contamination out of the WDS following
sensor activation. Two risk-based objectives (i.e., CVaR of flushedcontamination mass and CVaR of WDS time-to-return to normal condi­
tions), and a cost function (the number of activated hydrants), weredeemed objectives of the multi-objective optimization in generating thetrade-off curve (Pareto solutions) of optimal contamination response
strategies for each cluster. The time for the WDS to return to normal
conditions was equal to the time interval between contamination in­jection and when contamination concentration fell below a predefinedcontamination threshold (0.01 mg Ar⋅L− 1) at all nodes until the end of
the simulation duration.

(2)hi − P0 − Ei = RDi qeDi
where
Ei Node i elevation,
hi Hydraulic head at node i (= pi + Ei), and/RDi Virtual link’s resistance coefficient (= (Pf − P0 ) De ).
iThis equation can be integrated with Todini’s Global Gradient Al­
gorithm (GGA) for the hydraulic analysis system of equations to findpressure-driven demands (qD ), or the flows in virtual links, honoring
Wagner’s equation constraints. The clear advantage of this method isthat it does not increase the computational burden of the hydraulicsimulation, as the number of actual links and the size of the coefficientmatrix in GGA remain unchanged (Todini and Pilati, 1988; Rossman
et al., 2020). Other methods have been proposed for pressure-driven
hydraulic simulations, such as the Modified Pressure-Deficient
Network Algorithm (M-PDNA) (Babu and Mohan, 2012). However, as
most require an iterative hydraulic simulation or increase the number oflinks and reservoirs, hydraulic simulation runtime may increase
considerably. Pressure-driven hydraulic formulations were incorporated
into the EPANET-MATLAB®-Toolkit GGA for hydraulic simulation(Eliades et al., 2017).

Step 6 Pareto fronts post-processing: Integration and conflict resolution
model (CRM) development
After obtaining trade-off curves of strategies for clusters, a CRM wasdeveloped to select the optimal compromise strategy with the consensus
of the DMs. To do so, clusteral optimal strategies were first combined toassemble comprehensive contamination management strategies and therelated Pareto front. This integration was performed by defining threecomprehensive objectives: (i) minimizing the total number of activatedhydrants, (ii) maximizing the weighted average of CVaRs of the flushed
mass, and (iii) maximizing the weighted average of percentage reductionin CVaRs of time-to-return to normal conditions. Having achieved acomprehensive Pareto front, GMCR served as the decision supportmodel to identify the final strategy. The DMs and their options were
defined to attain possible states and after removing states that were notfeasible, the preferences of those remaining were specified for DMs toperform a forward GMCR analysis and reach an equilibrium state. By
associating DMs’ options with the corresponding intervals of compre­
hensive objectives, the optimal compromise contamination manage­
ment strategy was obtained.

3.2. Conditional value-at-risk (CVaR)
CVaR is a consistent risk measure that satisfies the set of obligatory
axioms and features defined by Artzner et al. (1999). CVaR emerged
because the measure of risk provided by the Value-at-Risk (VaR) could
only satisfy these axioms for a normal distribution of losses. Accord­
ingly, VaR and CVaR are defined as the value of the loss at a certain
confidence level (β), and the average of losses greater than VaR,
respectively (Rockafellar and Uryasev, 2002). Further information
regarding VaR, CVaR, and their formulation is provided in section S2 of
the Supplementary Materials.
3.3. Non-dominated Sorting Genetic Algorithm-III (NSGA-III)

3. Theory/calculations

NSGA-III, an extension of NSGA-II, was proposed to improve thequality of trade-off curves generated in a multi-objective optimization
process. The NSGA-III provides a set of reference points uniformly
distributed on the normalized hyperplane, thereby associating each so­lution with its closest reference point. The number of solutions assigned
to each reference point is termed the niche count. In applying the

The main theories and formulations of this study are further
described below.

4M. Shoorangiz et al.

Journal of Environmental Management 334 (2023) 117463• Pdfs Discrete probability of -FM or TTR value for sth feasible injection
scenario,• Qhti Actual discharge rate at hth hydrant during ith flushing interval,• T WDS return time to normal conditions,• Tinj Contamination injection time,• Tsim Simulation duration,• TTRfs WDS Time-to-Return to normal conditions for sth feasible in­jection scenario,• β Confidence level (0 ≤ β ≤ 1), and• ξβ VaR at confidence level β.

algorithm, the niche count of reference points is calculated, and solu­
tions associated with reference points having the lowest niche counts are
selected from the last front (Deb and Jain, 2014). This optimization al­
gorithm’s efficacy is best manifested in complex problems with many
objectives and non-dominated solutions, where maintaining the di­versity of the Pareto front is difficult. For further information and ap­
plications of NSGA-III, refer to section S3 of the Supplementary
Materials or Deb and Jain (2014), Ishibuchi et al. (2016), Campos Ciroet al. (2016), Liao et al. (2018), and Yannibelli et al. (2020).
3.3.1. Risk-based multi-objective optimization model
For each cluster, NSGA-III considers the number and location of
hydrants as binary decision variables, constrained by a predefined
number of maximum open hydrants. This number was determined basedon sensitivity analysis of the flushed contamination mass versus thenumber of hydrants and served as a constraint to narrow down the de­cision space and improve runtime. Optimization formulations for
obtaining a Pareto optimal front of clusters are as follows:
MaxCVaRβFM =MinCVaRβ− FM =ξβ− FM +Min CVaRβTTR = ξβTTR +]+1 ∑[− FMfs − ξβ− FM Pd−fs FM
1− β Nfs

]+ fs
1 ∑[
TTRfs − ξβTTR PdTTR
1 − β Nfs

Since flushed contamination mass is profit-based and its maximiza­tion was of interest (Eq. (3)), the optimization algorithm employed its
negative values to calculate loss-based CVaR, and minimized this value.
After obtaining clusteral trade-off curves, the following equations
were used to form the comprehensive trade-off curve:∑
Nfs × CVaRFM
clusters∑
Max(Average CVaRFM ) = Max
(12)
Nfs

(3)

clusters∑

Nfs × PRCVaRTTR
Max(Average PRCVaRTTR ) = Max clusters ∑
Nfs

(4)

clusters(5)

Min f3 = Nhyd

Nphyd

∑

(6)

subject to:

CVaRFM = − CVaR− FM(7)

PRCVaRTTR =[TTRfs −]+
ξβ− FM =]+ξβTTR =

Nhyd Nfi
∑
∑

FM =
h=1

(14)

CVaRNA
TTR − CVaRTTR
× 100
CVaRNA
TTR

(15)

h=1

Nhyd ≤ Max Nhyd

− FMfs −Eh

Min f3 =subject to:

[

(13)

{

{

β
− FMfs − ξβ− FM − FMfs − ξ− FM > 00− FMfs − ξβ− FM ≤ 0β
TTRfs − ξβTTR TTRfs − ξTTR > 00

TTRfs − ξβTTR ≤ 0hCout
× Qhti × ti
ti

where
CVaRNA
TTR Value of CVaRTTR when no hydrant is open (no action),
Eh Binary variable; 0 if the hydrant is not activated in any of theclusters and 1 otherwise,
Nphyd Number of potential hydrants, and
PRCVaRTTR Percentage reduction in CVaRTTR .

(8)

(9)

3.4. Graph model for conflict resolution (GMCR)

(10)

i=1

⃒(
)
N
⃒
nodes
∑
⃒
TTR = Min T ⃒ T ≤ ∀t ≤ Tsim
N(i, t) = 0 − Tinj⃒
i=1

A conflict is similar to a chess game where each player contemplatespossible moves and countermoves (Xu et al., 2018). Accordingly, aconflict can be visualized by a graph model in which each vertex rep­resents a state, and each arc shows a unilateral movement of a DM be­
tween two states (Kilgour et al., 1987). The GMCR approach was first
introduced by Fraser and Hipel (1979) and was further developed andimproved over time by Kilgour (1984), Fraser and Hipel (1984), andFang et al. (1993, 2003). The model’s main objective is to resolve
real-world conflicts based on game theory concepts. This decision sup­port framework was implemented in the GMCR+ computer software
suite, rendering it much more convenient to apply (Kilgour and Hipel,
2005; Kinsara, 2014).
Five solution concepts or stability definitions are included in
GMCR+. These define the probable real-world behaviors of DMs inachieving stable conflict resolutions and include Nash Stability (Nash),
General Metarationality (GMR), Symmetric Metarationality (SMR),
Sequential Stability (SEQ), and Simultaneous Stability (SIM) (Nash,
1950, 1951; Howard, 1971; Fraser and Hipel, 1979, 1984). For further
information about GMCR, solution concepts, and applications of GMCR
in environmental and water resource management conflicts, refer tosection S4 of the Supplementary Materials or Hipel et al. (1997, 2008),
Madani (2010), and Taravatrooy et al. (2019).

(11)

where
• f3 Number of open hydrants,• ti Duration of ith flushing interval (based on quality step),• Choutt i Contaminant concentration at hth hydrant during ith flushinginterval,• CVaRβFM Conditional value-at-risk of Flushed contamination Mass
(FM) at confidence level β,• CVaRβTTR Conditional value-at-risk of WDS Time-to-Return to normal
conditions (TTR) at confidence level β,• − FMfs Negative flushed mass value for sth feasible contamination
scenario,• N Binary variable; 0 if contaminant concentration is below thecontamination threshold and 1 otherwise,• Nfi Number of flushing intervals,• Nfs Number of feasible injection scenarios in a cluster,• Nhyd Number of open hydrants,• Nnodes Number of WDS nodes,

5M. Shoorangiz et al.

Journal of Environmental Management 334 (2023) 117463

4. Case study
The proposed methodology was applied and evaluated for the WDS
in the city of Lamerd (Fars Province, Iran). This WDS comprises 148
nodes, 174 pipes, 2 reservoirs, and 1 supply tank (Fig. 2).
Contamination management strategies were evaluated for Lamerd’s
WDS using the pressure-driven hydraulics EPANET model and consid­
ering a set of representative contamination events. Table 1 provides
information about the EPANET model parameters.
Arsenic (Ar), a well-known toxic contaminant and the most abundant
pollutant in drinking water globally, was selected as the model
contaminant in this study (Lytle et al., 2004; World Health Organization,
2011, World Health Organization, 2022). To compose the set ofcontamination events, 17 locations were selected as potential injection
nodes, of which 14 were zero-base-demand nodes, 2 were reservoirs,
and 1 was a tank. Fig. 2 shows the location of potential injection nodes
and hydrants within the Lamerd WDS. Based on the daily water demand
pattern, 14 different injection times (01:00, 07:00, 08:00, 09:00, 11:00,
12:00, 13:00, 14:00, 15:00, 16:00, 17:00, 18:00, 19:00, and 21:00) werechosen to cover possible times of injection during the day. Representing
Lamerd WDS’s daily water demand pattern, Fig. 3 shows peak demand
to occur between 12:00 and 13:00 and minimum demand between 15:00
and 16:00.
Injection times were selected such that more alternatives existed
during peaks of demand when health risks to water consumers weregreater. Although contamination has a lower impact at midnight due to
low water consumption, lack of sunlight and surveillance may bringsuitable conditions to sabotage the distribution system at such an hour.
Therefore, a midnight injection time was also investigated for a morethorough flushing strategy evaluation. Four injection durations (1, 2, 3,
and 6 h) were considered, with a constant 20 kg of contaminant uni­
formly entering the WDS during these intervals. These 17 (locations)×
14 (times) × 4 (durations) = 952 events were simulated to derive WQMs.
Several scenarios that resulted in no significant trace of contamination
remaining in the WDS were identified. These events, in which thecontaminant had been injected into the distribution system tank, wereomitted since the tank does not always play a role in supplying water,
and injecting the contaminant outside its operating hours would notbring about any system contamination. Finally, a total of 928

Fig. 3. Hourly multipliers of the base water demand.
Table 1EPANET simulation model parameters.
Parameter

Value

Simulation duration (hours)
Hydraulic step (minutes)
Quality step (minutes)
Pattern step (hours)
Pipes Hazen-Williams roughness coefficient
Pipes diameter (mm)Contaminant wall reaction coefficientContaminant bulk reaction coefficient (day− 1)

48
5
5
1
145
50–8000− 0.05contamination events made up the set of representative contamination
events directing performance evaluation of the proposed contamination
management methodology. Notably, the maximum WDS return time tonormal conditions with no action was 46 h from the onset of an event
simulation; therefore, a simulation duration of 48 h was consideredsufficient (Table 1).

Fig. 2. Location of Lamerd and a plan of its water distribution system. Potential injection nodes and hydrants are marked. adapted from "Optimal and objective
placement of sensors in water distribution systems using information theory" by Khorshidi et al., 2018. Water Research, 143: 218-228. Adapted with permission
from Elsevier.
6

M. Shoorangiz et al.

Journal of Environmental Management 334 (2023) 117463

5. Results and discussion

MATLAB® parallel computing tool and functions (such as parfor andspmd). Each CPU core provided a separate worker in MATLAB®, andwater quality simulations took place simultaneously on these workers.
Finally, RSOM was executed to obtain the trade-off curve of optimal
flushing strategies for each cluster. The Pareto optimal fronts are shownin Fig. 5, while Table 2 provides a summary of the results.
A confidence level (β) of 0.95, a typical value for VaR-CVaR calcu­
lations, was employed (Khorshidi et al., 2019). The population size andnumber of generations of the NSGA-III optimizer were 92 and 80,
respectively. According to Deb and Jain (2014), the smallest multiple offour larger than the number of reference points is a sufficient population
size for NSGA-III. Having 3 objectives and 12 divisions along eachobjective axis, 91 uniformly distributed reference points were plotted on
the normalized hyperplane; accordingly, a population size of 92 was
sufficient. The maximum number of generations was selected based on a
pre-analysis of the convergence of the integrated RSOM.
To evaluate the efficacy of events grouping and parallel water quality
simulations in runtime reduction, two additional models were devel­
oped and evaluated for the 3rd cluster. The first model only incorporated
events grouping, while the second had neither parallel computation nor
events grouping. The RSOM runtime for the 3rd cluster was 3.4 days
when employing parallel computation and events grouping (Table 2),
11.2 days without parallel processing, and 16.3 days without either
parallel processing or events grouping. The parallel water quality
simulation clearly had the greatest impact on reducing RSOM runtime,with a reduction of 70% (from 11.2 to 3.4 days), while events grouping
was able to reduce this time by 31% (from 16.3 to 11.2 days), with the
combination of the two approaches decreasing runtime by 79% (from16.3 to 3.4 days).
Once clusteral trade-off curves were obtained, they were merged toform comprehensive contamination management strategies. To pointout the diverse non-dominated comprehensive flushing strategies,
NSGA-III was fed 26 × 36 × 25 × 14 = 327600 integrated strategies (i.
e., the decision space). Of these, only 253 non-dominated solutions
remained by domination check of three comprehensive objectives (Eqs.
(12)–(14)), representing about 0.08% of the decision space. Fig. 6adisplays the trade-off curve of all non-dominated strategies, while
Fig. 6b shows 28 diverse non-dominated solutions obtained via refer­ence point association with 6 divisions along objective axes and, sub­sequently, 28 reference points.
In the last step, GMCR was employed to model the potential conflictamong DMs and reach one final strategy. Table 3 illustrates the DM
options regarding flushing system implementation. With 6 specified
options, a total of 26 = 64 states (each option may (Y) or may not (N) be
selected) could be formed by different combinations of options; how­ever, some would not be feasible in practice.
To exclude states that were not feasible, DMs could either adopt oneof their options or none at all ("YN", ’’NY", or "NN′′ ). Moreover, it would
be irrational for LWWC and LHN to take no actions when MoE agreed to
implement a project at a full scale ("YNNNNN′′ ). Based on these as­
sumptions, 26 feasible states remained after omitting non-feasible ones
(Table S3). The relative preferences of feasible states for DMs were thendefined. In short, the MoE preferred the project to be as small-scale andlimited as possible for a lower financial cost, while it would rather theother two DMs choose their strictest options. As not only the project
financing, but its performance, is mostly the MoE’s responsibility, thelowest preference would be if both the other DMs chose not to pursue
either one of their options, leading to a poor performance of the flushingsystem in terms of their associated objective functions. The LWWC andLHN preferred to execute their first and strictest option regardless of the
MoE’s financing. Simultaneously, they preferred the other DM (LHN or
LWWC) to apply either one of its options to have a better overall flushingefficiency. The LWWC and LHN would have the weakest preference
when they chose neither one of their options and faced their low-yieldobjectives. Based on the DMs’ preference profiles, GMCR ranked thefeasible states as follows:

The RSOM codes were written in MATLAB® and executed on acomputer with an Intel® Core™ i9-10900X CPU (only 6 out of 10 cores
were dedicated to this study on a virtual machine) and 20.0 GB DDR 4
RAM.
Upon obtaining the set of representative contamination events, an
optimal layout of sensors was devised by implementing a binary variable
single-objective genetic algorithm (population size = 500; generations= 100, according to preliminary runs) to minimize the average time-todetect of contamination events as the objective of this optimization, witha minimum number of sensors having 100% detection probability. All
WDS nodes were considered potential sensor nodes - binary decisionvariables, with an Ar detection threshold of 0.01 mg⋅L− 1. Fig. S3 of the
Supplementary Materials shows the average time-to-detect of optimal
sensor layouts as a function of the number of sensors defined as aconstraint. The knee point, corresponding to locating four sensors acrossthe WDS with an average time-to-detect of 1.49 h, was chosen and is
illustrated in Fig. S4.
Based on the typical flow rate of public hydrants, their discharge rate
was set to 44 L⋅s− 1 (700 gal⋅min− 1) at 138 kPa (20 psi) residual pressure(Brakhage and Miller, 2015). To address physical limitations, a 1-h delay
was assumed to occur between activating hydrants and sensor activa­tion. A sensitivity analysis was carried out to determine the maximum
number of open hydrants that would make a significant change in
CVaRFlushedmass . Different hydrant numbers with the same flushingduration (3 h) were given to a binary variable genetic algorithm and thesensitivity of optimal CVaRFlushedmass to the number of open hydrants wasevaluated. A maximum of five hydrants were shown to be sufficient for
flushing the contaminant out of the WDS, and opening additional hy­
drants only increased CVaRFlushedmass by at best 10% (Supplementary
Materials, Fig. S5a). Similarly, a sensitivity analysis was performed todetermine a sufficient flushing duration; this was selected to be 4 h(Fig. S5b).
To conduct the frequency analysis needed for risk-based calculations,scenarios must be screened before the integrated RSOM can be devel­
oped. Table S2 (Supplementary Materials) illustrates the number offeasible contamination events in each cluster forming the basis of the
RSOM.
To speed up RSOM executions, two approaches were used inter­
connectedly: (i) grouping scenarios with similar hydraulic characteris­tics to assign a single hydraulic simulation to all scenarios in a group,and (ii) parallel water quality simulation of group scenarios. Contami­
nation events in each cluster were grouped based on their detection
time, as a similar detection time meant the same hydrant opening timeand identical WDS hydraulic behavior, regardless of each event’s spe­cific injection characteristics. The number of hydraulic analyses was
reduced by at least 85% using this approach (Fig. 4).
Parallel water quality simulation was carried out using the

Fig. 4. The number of events and groups of events, and the percentage
reduction in hydraulic simulations as a function of cluster number.
7

M. Shoorangiz et al.

Journal of Environmental Management 334 (2023) 117463

Fig. 5. Trade-off curves of optimal flushing strategies for: (a) 1st cluster, (b) 2nd cluster, (c) 3rd cluster, and (d) 4th cluster.

reachability criteria. This equilibrium reflects the situation where both
LWWC and LHN choose their firmest principles, while the MoE decides
to fund a limited-scale project ("NYYNYN′′ ). To end the conflict and finda compromise strategy, a Pareto optimal solution that satisfied theconditions of S14 (options chosen by DMs) should be sought in thecomprehensive strategies trade-off curve. Based on expert opinion,
Table S4 illustrates the corresponding intervals of comprehensive
objective values for possible DM options. An investigation of thecomprehensive strategies of the Pareto optimal front (Fig. 6a) revealed
that no available strategy corresponded to S14. Therefore, while S14 wasa reachable equilibrium in the conflict, it was not feasible in practice andother equilibrium states had to be found under coalitions of DMs.
In the case where LWWC and LHN formed a coalition, two additional
states, S17 and S23, would satisfy all solution concepts. These states re­

Table 2
Summary of risk-based integrated model results.
Parameter

1st cluster

2nd cluster

3rd cluster

Min CVaRβFM (kg)

0

0

0.03

Max CVaRβFM (kg)4.58

Min CVaRβTTR (h)21.83

Avg. CVaRβTTR (h)CVaRNA
TTR (h)RSOM runtime (d)Number of optimal
strategies

43.89
4.73
26

Avg. CVaRβFM (kg)Max CVaRβTTR (h)

3.33

43.27

23.40

2.16

0.69

19.92

31.89

21.76

33.32
6.86
36

2.71

1.52

24.08

31.13

26.88

34.55
3.39
25

4th cluster
0

2.65

1.90

6.44

7.89

6.97

8.01
4.72
14

MoE : s14 ≻1 (s17 ∼1 s23 )≻1 s26 ≻1 (s5 ∼1 s11 )≻1 (s8 ∼1 s20 )≻1 s12 ≻1 (s21 ∼1 s15 )≻1 s24 ≻1 (s3 ∼1 s9 )≻1 (s6 ∼1 s18 )≻1 s13 ≻1 (s22 ∼1 s16 )≻1 s25 ≻1 (s4 ∼1 s10 )≻1 (s7 ∼1 s19 )≻1 s2 ≻1 s1
LWWC : (s12 ∼2 s13 ∼2 s14 ∼2 s21 ∼2 s22 ∼2 s23 )≻2 (s3 ∼2 s4 ∼2 s5 )≻2 (s15 ∼2 s16 ∼2 s17 ∼2 s24 ∼2 s25 ∼2 s26 )≻2 (s6 ∼2 s7 ∼2 s8 )≻2 (s1 ∼2 s2 ∼2 s9 ∼2 s10 ∼2 s11 ∼2 s18 ∼2 s19 ∼2 s20 )LHN:(s12 ∼3 s13 ∼3 s14 ∼3 s15 ∼3 s16 ∼3 s17 )≻3 (s9 ∼3 s10 ∼3 s11 )≻3 (s21 ∼3 s22 ∼3 s23 ∼3 s24 ∼3 s25 ∼3 s26 )≻3 (s18 ∼3 s19 ∼3 s20 )≻3 (s1 ∼3 s2 ∼3 s3 ∼3 s4 ∼3 s5 ∼3 s6 ∼3 s7 ∼3 s8 )flected a similar situation to S14 where either LWWC or LHN would
consent to take balanced measures instead of strict ones. Nevertheless,both new equilibria remained non-feasible since there was no solution to
the comprehensive strategies trade-off curve satisfying S17 or S23 con­ditions. It was quite clear that in order to achieve a practical, feasible
equilibrium, a coalition had to be formed between the MoE and either
LWWC or LHN. Under this coalition, S12 and S13 would become stable,
satisfying all stability definitions as the MoE agreed to provide more
funding for the stringent requirements of LWWC and LHN.
With no coalition, S12 ("NNYNYN′′ ) seemed the best compromise
state. The inverse GMCR analysis revealed that according to DM pref­
erences, only one unsatisfied condition existed for S12 to become an

The conflict was then modeled by forward GMCR analysis. Fig. 7
shows the three-step tree graph of unilateral improvements beginningfrom S1, the presumed status quo.
Table 4 presents the stability results for all solution concepts.
Omitted states were not stable under any stability definition. As illus­
trated, S14 satisfied all solution concepts, while S12 and S13 were only
able to satisfy the GMR and SMR concepts. Therefore, S14 was selected as
the most stable state or equilibrium.
Based on Fig. 7, S14 is reachable from the status quo solely by uni­lateral improvements, and therefore satisfies both stability and8M. Shoorangiz et al.

Journal of Environmental Management 334 (2023) 117463

Fig. 6. Trade-off curves of non-dominated comprehensive strategies: (a) all strategies, and (b) 28 diverse strategies.

northern areas of the WDS and, according to Table 5, provide adequate
coverage against contamination intrusion from potential sources.
Overall, 10 out of 23 (< 45%) potential hydrant nodes were selected toflush contamination out of the WDS following sensor activation. Fig. S7shows a color map of water consumer distribution in Lamerd’s WDS.
Optimal hydrant locations are in close agreement with the distributionof population among the demand nodes. In other words, although nodal
demands had no direct role in the proposed model, hydrants have been
located near densely populated areas. This would likely enhance con­sumer safety in more crowded areas and at critical demand nodes.
Moreover, comparing Fig. S4 to Fig. 8 reveals that hydrants in eachcluster are close to the associated sensor location. For the 1st cluster,
however, hydrants 2 and 4 are relatively far from the sensor, indicating
the large distance of propagation of contamination following its detec­tion and the need for hydrants at greater distances. Lastly, investigating
the average performance of the final optimal strategy (Table 5) suggeststhat despite using risk-based formulations in the optimization algorithm,
the average efficacy is quite acceptable. The average clusteral contam­
ination flushed mass was between 7 kg and 12.26 kg (35–61.3% of inputmass), while the average TTR was reduced by 53.7, 31.6, 60.2, and14.4% for clusters 1 to 4, respectively.

Table 3
Decision-maker options.
Decision-makers

Options

1. Ministry of Energy (MoE)

1. Implementing a full-scale flushing system
2. Implementing a limited-scale flushingsystem
3. Imposing strict measures for the quickest
WDS normalization
4. Taking balanced measures for WDS
normalization
5. Adopting a rigorous contamination
flushing policy
6. Defining minimum levels of flushingsystem efficiency

2. Lamerd Water and Wastewater
Company (LWWC)
3. Lamerd Health Network (LHN)

equilibrium state, preferring S12 over S14 by the MoE. This means that
the MoE should be convinced to provide more funding, at least for a midscale project. To do so, joint meetings could be held with all DMs andindependent experts (mediators) to explain the significance of the
project and its impact on stakeholder health. Although this would provea difficult task, S12 could be finally achieved by presenting the study
results with the help of a mediator. Based on Fig. S6, S12 would bereachable from the status quo after MoE’s preference adjustment, suchthat it would satisfy both stability and reachability criteria. Two corre­
sponding strategies existed in the total non-dominated strategies tradeoff curve; however, there was only a single one in the diverse curve.
Table 5 presents the characteristics and performance of this compre­
hensive contamination flushing strategy, and Fig. 8 displays the corre­
sponding hydrant locations.
As illustrated, hydrants are spread throughout the central andTable 4
Stability results.
Solution concept

S12

S13

S14

Nash
GMR
SMR
SEQ
SIM

–
Y
Y
–
–

–
Y
Y
–
–

Y
Y
Y
Y
Y

Fig. 7. The conflict’s three-step tree graph of unilateral improvements.
9M. Shoorangiz et al.

Journal of Environmental Management 334 (2023) 117463

Table 5Characteristics and performance of the final compromise contamination flushing strategy.
Final compromise strategy’s overall performance
Num. hydrants

Avg. CVaRFM (kg)Avg. CVaRTTR reduct. (%)

10

2.72

34.01

Final compromise strategy’s clusteral performance
Cluster

Hydrants index

CVaRFM (kg)Avg. FM (kg)CVaRTTR (h)Reduct. CVaRTTR (%)

Avg. TTR (h)Avg. TTRNA (h)

1
2
3
4

2, 4, 7, 8, 9
1, 2, 4, 6, 7
2, 3, 4, 5, 6
9, 10

4.56
2.16
2.71
1.77

12.26
7.91
9.43
7.00

22.58
20.93
24.20
6.69

48.55
37.18
29.96
16.49

7.39
8.90
6.40
3.26

15.97
13.01
16.09
3.81

objectives, and modeling the final decision-making process by GMCR,
taking a comprehensive viewpoint for each strategy’s performance into
account. This framework proved viable in finding solutions to contam­
ination threats in a real-world WDS in a timely manner.
Future research may consider studying dynamic optimization ofcontamination flushing hydrant placement; that is, dividing the flushingduration into a number of intervals and optimizing the location of hy­
drants for each separately. Moreover, water consumer behavior could beincorporated into the risk-based simulation and/or optimization using
agent-based models, leading to an even more comprehensive and real­istic study with network-specific circumstances. Including the goals andpriorities of DMs directly in the integrated model by various existing
formulations and techniques such as the leader-follower game could be
another intriguing research line.
Authors contribution
Mostafa Shoorangiz: Conceptualization, Methodology, Coding,
Software, Validation, Investigation, Data curation, Writing – originaldraft, Visualization. Mohammad Reza Nikoo: Conceptualization, Meth­odology, Supervision, Coding, Software, Validation, Investigation, Data
curation, Writing – original draft, Visualization. Jirka Šimůnek:Conceptualization, Methodology, Validation, Writing – review & edit­
ing. Amir H. Gandomi: Conceptualization, Methodology, Writing – re­view & editing. Jan Franklin Adamowski: Validation, Writing – review &editing. Malik Al-Wardy: Validation, Writing – review & editing.

Fig. 8. Final optimal locations of hydrants.

6. Conclusions
This study presented a comprehensive framework for emergency
contamination flushing during contamination events in an urban Water
Distribution System (WDS). This framework included the EPANET hy­
draulic and water quality simulation model linked with a risk-based
NSGA-III optimization algorithm and the Graph Model for Conflict
Resolution (GMCR) to identify a final compromise strategy. Accord­
ingly, a set of hazardous contamination scenarios was highlighted,
taking critical injection nodes, times and durations into account. Then,four sensors were optimally placed in the WDS to minimize the averagetime-to-detect of contamination events, constituting four clusters of
scenarios based on the first sensor activated following contamination
intrusion. Clusteral Pareto optimal flushing strategies were subsequently
found by coupling EPANET with a novel risk-based optimization model
considering three objectives, each attributed to a different DecisionMaker (DM). Afterward, the comprehensive Pareto front was obtainedby integrating clusteral Pareto fronts using three comprehensive objec­
tives. Lastly, the potential conflict between DMs was modeled by GMCR,
and a final compromise flushing strategy was identified. The perfor­
mance of the proposed framework was evaluated in the WDS of Lamerd
in Fars province, Iran. Results showed the framework capable ofpointing out a final compromise strategy that minimized the risks
associated with contamination threats in the WDS and minimized the
project cost in terms of the number of hydrants. This study’s contribu­
tion includes introducing a hybrid scenario grouping-parallel water
quality simulation technique to significantly reduce online simulationoptimization model runtime (~80%), considering uncertainties ofcontamination intrusion using Conditional Value-at-Risk (CVaR)-based

Funding
This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jenvman.2023.117463.
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