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gis projects for civil engineeing students
Environmental Modelling & Software
Volume 22, Issue 7, July 2007, Pages 951965
IRA-WDS: A GIS-based risk analysistool for
water distribution systems
K. Vairavamoorthya,b, , [Author Vitae], Jimin Yana[Author Vitae], Harshal M. Galgalea[Author Vitae], Sunil D. Gorantiwara[Author Vitae] a Department of Civil and Building Engineering, Loughborough University, LE11 3TU,
UK
b Department of Urban Water and Sanitation, UNESCO-IHE Institute for WaterEducation, P.O. Box 3015, 2601 DA, Delft, The Netherlands
Received 16 June 2005. Revised 30 March 2006. Accepted 30 May 2006. Availableonline 13 December 2006.
http://dx.doi.org/10.1016/j.envsoft.2006.05.027 ,How to Cite or Link Using DOI Cited by in Scopus (6) Permissions & Reprints
Abstract
http://www.sciencedirect.com/science/journal/13648152http://www.sciencedirect.com/science/journal/13648152http://www.sciencedirect.com/science/journal/13648152/22/7http://www.sciencedirect.com/science/journal/13648152/22/7http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff1http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff1http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff2http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff2http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff2http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt1http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt1http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt1http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff1http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff1http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt2http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt2http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt2http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff1http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff1http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt3http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt3http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt3http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff1http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff1http://www.sciencedirect.com/science/article/pii/S1364815206001538#aff1http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt4http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt4http://www.sciencedirect.com/science/article/pii/S1364815206001538#vt4http://dx.doi.org/10.1016/j.envsoft.2006.05.027http://dx.doi.org/10.1016/j.envsoft.2006.05.027http://www.sciencedirect.com/science/help/doi.htmhttp://www.sciencedirect.com/science/help/doi.htmhttp://www.sciencedirect.com/science/help/doi.htmhttp://www.sciencedirect.com/science?_ob=RedirectURL&_method=outwardLink&_partnerName=656&_eid=1-s2.0-S1364815206001538&_pii=S1364815206001538&_origin=article&_zone=art_page&_targetURL=http%3A%2F%2Fwww.scopus.com%2Finward%2Fcitedby.url%3Feid%3D2-s2.0-33846862440%26partnerID%3D10%26rel%3DR3.0.0%26md5%3D2a759fc2b96c2232aeca5c545a57f7f9&_acct=C000238598&_version=1&_userid=11019620&md5=9ed86c7ed500cbb1666c9ebe308c934bhttp://www.sciencedirect.com/science?_ob=RedirectURL&_method=outwardLink&_partnerName=656&_eid=1-s2.0-S1364815206001538&_pii=S1364815206001538&_origin=article&_zone=art_page&_targetURL=http%3A%2F%2Fwww.scopus.com%2Finward%2Fcitedby.url%3Feid%3D2-s2.0-33846862440%26partnerID%3D10%26rel%3DR3.0.0%26md5%3D2a759fc2b96c2232aeca5c545a57f7f9&_acct=C000238598&_version=1&_userid=11019620&md5=9ed86c7ed500cbb1666c9ebe308c934bhttp://www.sciencedirect.com/science?_ob=RedirectURL&_method=outwardLink&_partnerName=936&_eid=1-s2.0-S1364815206001538&_pii=S1364815206001538&_origin=article&_zone=art_page&_targetURL=https%3A%2F%2Fs100.copyright.com%2FAppDispatchServlet%3FpublisherName%3DELS%26contentID%3DS1364815206001538%26orderBeanReset%3Dtrue&_acct=C000238598&_version=1&_userid=11019620&md5=8173b5f227283b73b5e35b79f776c94fhttp://www.sciencedirect.com/science?_ob=RedirectURL&_method=outwardLink&_partnerName=936&_eid=1-s2.0-S1364815206001538&_pii=S1364815206001538&_origin=article&_zone=art_page&_targetURL=https%3A%2F%2Fs100.copyright.com%2FAppDispatchServlet%3FpublisherName%3DELS%26contentID%3DS1364815206001538%26orderBeanReset%3Dtrue&_acct=C000238598&_version=1&_userid=11019620&md5=8173b5f227283b73b5e35b79f776c94fmailto:[email 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This paper presents the development of a new software tool IRA-WDS. This GIS-based software
predicts the risks associated with contaminated water entering water distribution systems fromsurrounding foul water bodies such as sewers, drains and ditches. Intermittent water distribution
systems are common in developing countries and these systems are prone to contamination when
empty. During the non-supply hours contaminants from pollutionsources such as sewers, open
drains and surface water bodies enter into the water distribution pipes through leaks and cracks.Currently there are no tools available to help engineers identify the risks associated with
contaminant intrusion into intermittent water distribution systems. Hence it is anticipated that
IRA-WDS will find wide application in developing countries. The paper summarises the detailsof the mathematical models that form the basis of IRA-WDS. It also describes the software
architecture, the main modules, and the integration with GIS using a tight coupling approach. A
powerful GUI has been developed that enables data for the models to be retrieved from thespatial databases and the outputs to be converted into tables and thematic maps. This is achieved
seamlessly through DLL calling functions within the GIS. This paper demonstrates the
application of the software to a real case study in India. The outputs from IRA-WDS are risk
maps showing the risk of contaminant intrusion into various parts of the water distribution
system. The outputs also give an understanding of the main factors that contribute to the risk.
Keywords
Water supply; Risk assessment; Contaminant intrusion; Developing countries; Intermittent water supply; GIS; Tight coupling
1. Introduction
Contamination of drinking water due to exposure to biological and chemical pollutants is a major
cause of illness and mortality. Recent evidence has demonstrated that external contaminant
intrusion into a water distribution network may be more frequent and of a greater importancethan previously suspected (LeChevallier, 1999). The problem of external contaminant intrusion
is more aggravated in developing countries where the pollutionsources crisscross with water
distribution systems and intermittent water supplies are prevalent ( [Choe and Varley, 1997],
[Seckler et al., 1998]and[Rosegrant et al., 2002]).
A serious problem arising from intermittent supplies, which is generally ignored, is the
associated high level of contamination which occurs in networks where there are prolongedperiods of interruption of supply due to negligible or zero pressure in the system. Such problems
lead to increased health risks as water becomes contaminated with pathogens due to the intrusion
from surrounding foul water bodies (e.g. sewers, ditches), through joints and cracks in the
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deteriorated water distribution pipes. Hence in developing countries the distribution network has
become a point at which contamination frequently occurs to unacceptably high levels, posing athreat to public health ( [Swerdlow et al., 1992]and[Besser et al., 1995]).
Currently there are no software tools available to help engineers identify the risks associated with
contaminant intrusion into intermittent water distribution systems. The need for such tools hasresulted in the development of the Improved Risk Assessment of Water Distribution Systems
(IRA-WDS) tool. The application of this tool will enable engineers to better manage waterquality by developing appropriate control measures to minimise these risks by, for example,
prioritising their operational maintenance strategies.
Previous studies mainly focussed on risk due to breakage of pipes. All these risk assessment
methodologies are based on the principle of regression ( [Clark et al., 1982],[Shamir and
Howard, 1979],[Walski, 1987]and[Walski and Pelliccia, 1982]) or probability analysis (
[Andreou et al., 1987a],[Andreou et al., 1987b],[Deb et al., 1998],[Eisenbeis et al., 1999],[Herz, 1996],[Herz, 1998]and[Lei, 1997]) and need large amounts of historical data. But the
data needed for this purpose is extremely rare and insufficient ( [Ang and Tang,1984]and[Guymon and Yen, 1990]). Moreover these studies were developed for the waterdistribution systems which operate at high pressures where the problem is one of leakage rather
than contaminant intrusion. But, as stated earlier, pressures in water distribution systems in
developing countries are often negligible due to intermittent water supply and contaminants mayenter the system through the deteriorated pipes during the periods of low pressure. As the
techniques developed for risk assessment due to breakage do not highlight the contaminant
intrusion, these are not applicable for intermittent water distribution systems. The techniques
developed to consider contaminant intrusion under low or negative pressure resulting fromtransient use rely on the leakage or breakage data as a surrogate for the intrusion pathway (
[Boyd et al., 2004a],[Boyd et al., 2004b],[Funk et al., 1999]and[LeChevallier et al., 2003]).
But these methods have generally assumed that contaminant sources exist around all waterdistribution pipes and were applied to continuous systems.
Lindley and Buchberger (2002)developed a technique to identify the locations in the waterdistribution systems that may be susceptible to unintended contaminant intrusion. They argued
that three susceptibility conditions must be met for an intrusion into a water distribution system
to occur. These are: an adverse pressure gradient, an intrusion pathway and a contaminantsource. However, the technique developed by Lindley and Buchberger simply performs a spatial
analysis to produce a combination of the three susceptibility conditions and does not model the
susceptibility conditions for contaminant intrusion.
Unlike the previous methods, the IRA-WDS assesses the risk of contaminant intrusion into the
water distribution system by modelling the process of contaminant transport from
pollutionsources such as sewers, open drains and foul water bodies. In addition it includes apipe-condition assessment model that is used to indicate intrusion pathways. This model uses
over 20 indicators to estimate pipe condition.
Risk assessment of water distribution systems due to pollutionsources is a spatial process as the
risk components such as hazard(due to pollutionsources) and vulnerability (due to the water
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Gis Projects for Civil Engineeing Students
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distribution system) are both spatially diverse and need large volumes of spatially diverse data.
The pollutionsources such as the sewer system, open drains and surface water bodies are parts ofthe urban sanitation infrastructure and geo-spatially referenced. In addition, the vulnerability
(condition in terms of deterioration) of the water distribution system is spatially distributed since
the deterioration attributes are spatial in nature. The risk of contaminant intrusion into a water
distribution system is a product of the interaction between the hazard and vulnerabilitycomponents and is therefore a spatial process. Hence it is necessary to know the spatial outputs
in the form of maps representing the degree of severity of the associated risk to the water
distribution system so that decision makers can draw up priorities for the rehabilitation of thewater distribution system.
Geographical information systems (GIS) are powerful tools for handling spatial data, performing
spatial analysis and manipulating spatial outputs. A GIS also provides a consistent visualisation
environment for displaying the input data and results of a model. This ability of GIS is very
useful in a decision-making process. The integration of GIS and external models enables theutilisation of the advantages of both ( [Goodchild et al., 1992],[Goodchild et al., 1993],
[Goodchild et al., 1996],[Fotheringham and Rogerson, 1994],[Fischer et al., 1996],[Longleyand Batty, 1996],[Fotheringham and Wegener, 2000]and[Argent, 2004]). Since GIS allows theinput of spatial data into a model and provides the outputs in spatial forms, it was decided tointegrate a GIS into the tool developed for risk assessment. The combination of GIS and
modelling tools enables the conversion of large amounts of data into information and then into
practical knowledge that is useful for risk assessment. The intrinsic ability of GIS to store,analyze and display large amounts of spatial data enables it to make a significant contribution to
risk assessment.
This paper presents the development of a new software tool: the Improved Risk Assessment of
Water Distribution Systems (IRA-WDS). The software consists of three models: the contaminant
ingress model, the pipe-condition assessment model and the risk assessment model. The modelsare developed in the C++ language and integrated with the GIS using a tight-coupling approach.
This paper highlights the different integration methodologies available for the integration of
environmental models with GIS and describes the integration methodology used for developing
the IRA-WDS; the modelling tools of IRA-WDS and their integration with GIS. The uses ofIRA-WDS and a case study of its application are described.
2. Method
This section presents the methodology used for the improved risk assessment for water
distribution systems due to contaminant intrusion. Three risk factors have been identified for
contaminant intrusion into a water distribution system. These are: the section of pipe in thecontaminant zone (SPCZ), the contaminant concentration and the condition of the water
distribution pipes in terms of deterioration. These risk factors are established by the associated
models, namely, contaminant ingress (contaminant zone and contaminant seepage) and pipecondition assessment models. The three risk factors obtained from these models are combined to
obtain a risk index for each pipe by the use of a risk assessment model.
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Fig. 1presents the conceptual model for IRA-WDS. Contaminants from the pollutionsources
(e.g. leaky sewers, drains) seep through the soil and percolate down. In this process, thesepollutionsources develop a contamination zone (CZ) and if all or part of a water distribution
system's pipe work passes through the contamination zone, contaminants may find entry into the
pipes through such entry points as cracks and leaky joints. Hence, there is a risk of contaminant
intrusion if a water pipe lies in a contaminant zone and at the same time the condition of the pipehas been compromised so as to allow contaminant entry.
Fig. 1. Conceptual model for IRA-WDS.
Thus the methodology consists of developing and integrating the following three separate modelsas shown inFig. 2.
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Fig. 2. Components of IRA-WDS modelling tools.
2.1. Contaminant ingress
The contaminant ingress model estimates the contaminant zone of the pollutionsources (sewer
pipes, open drains/canals and surface water bodies), and identifies the section of the water
distribution pipes passing through the contaminant zone (SPCZ). This model consists of two
parts: the contaminant zone and contaminant transport.
The contaminant zone model predicts the envelope of pollution emanating from pollutionsources:the contaminant zone. The model is based on the seepage process, which forms part of the theory
of soil mechanics. It is assumed that the seepage of contaminants from pollutionsources such as
unlined canals/drains and surface water bodies follow a saturated flow while that from
pollutionsources such as sewer pipelines and lined canals/drains follows an unsaturated flow.The separate sets of equations for the estimation of a contaminant zone were developed for
saturated and unsaturated flows by modifying the approach presented byHarr (1962). The details
of the development of these equations can be found inYan et al. (2002). Once the contaminant
zones formed due to pollutionsources are established, the model uses spatial techniques toestimate the sections of the water distribution pipes that intersect with the contaminant zone
(SPCZ). This is achieved using geometric algorithms. The algorithms calculate the length ofcontaminated pipe (LC) in the contaminant zone using the upstream and downstream points ofintersection between the contaminant zone and the segment of the water pipe passing through the
contaminant zone, as given in Eq.(1):
(1)
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where LCk is the length of the kth pipe in the contaminant zone (m); upkand dpkare the upstream
and downstream intersection points of the kth pipe with the contaminant zone and NP is thenumber of water distribution pipes.
The contaminant transport model simulates the water flux and the variable concentration of the
contaminants within the contaminant zone and then predicts the contaminant loading on theSPCZ. This is done by modelling the contaminant transportation process through the soil from
the pollutionsources towards the water distribution pipes. Two separate models were developedfor estimation of the water flux: the unsaturated flow model for pollutionsources such as sewer
pipes and lined drains/canals and the saturated flow model for unlined drains/canals and surface
water bodies.
For the saturated flow model the flow region is divided into a flownet and for each cell of the
flownet the pore-water velocity vi is estimated by combining the velocity potential and Darcy's
equation (Fetter, 1999). For the unsaturated flow model, the pore water velocity is calculatedusing the water flux obtained from the GreenAmpt model and the projected water content
(Green and Ampt, 1911).
These pore-water velocities are then used by the contaminant transport model to calculate the
variable contaminant concentration in soil. In this paper, an analytical solution for the advection-
diffusion-reaction equation developed byBear (1972)is used, as given in Eq.(2):
(2)
where
where RC is the relative concentration, tis time (hours),z is the depth along the flow path (cm),
D is the dispersion coefficient (cm2/day), is the pore-water velocity (cm/h), b is the bulk
density (g/cm3); Kd is a sorption constant, n is the porosity, S is the solid-phase concentration
(mg/l), and is the first-order decay coefficient in the liquid phase (1/h).
The contaminant transport model estimates the profile of the contaminant concentration in thesoil. The contaminant concentration along the SPCZ is the average concentration at its start and
end intersection points as given by Eq.(3).
(3)
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where CCkis the average contaminant concentration along SPCZ of pipe k,RCkup is the
contaminant concentration at the upstream intersection point andRCkud is the contaminantconcentration at the downstream intersection point of pipe k.
2.2. Pipe condition assessment
The pipe condition assessment (PCA) model estimates the relative condition of the pipes in the
water distribution network to assess the potential intrusion pathway. The condition of each pipeis assessed by means of numerous factors related to the physical, environmental and operational
aspects of a water distribution system (seeTable 1). These factors are grouped into different
indicators at three levels, depending on the nature of the influence of each factor on the
deterioration process of the pipe. The uncertainties inherent in these pipe condition indicators aredescribed using fuzzy set theory (Zadeh, 1965).
Table 1. Pipe indicators and their groups at different levels
Description Level 1 Level 2 Level 3 FinalHazenWilliam coefficient of
friction (C) is considered to
characterise this influence
Material decay
Pipe indicators
Physical
indicatorsPCA
Larger diameter pipes are lessprone to failure than smaller
diameter pipes
Diameter (mm)
Larger length pipes are more
prone to failure than smallerlength pipes
Length (m)
The pipes having internalprotection by lining and/or
coating are less susceptible tocorrosion
Internal
protection
The pipes having external
protection by lining and/or
coating are less susceptible todeterioration
External
protection
Improper bedding may result
in premature pipe failure
Bedding
condition
Installationindicators
Poor workmanship may
deteriorate the pipes andcause more risk regardless of
pipe age and other factors
Workmanship
Some types of jointsexperience premature failure
(e.g. leadite joints)
Joint method
The more the joints a pipe No. of joints
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Description Level 1 Level 2 Level 3 Final
has, the more risk of the pipegetting structurally worse
The effects of pipe
degradation become more
apparent over time
Year of
installation
Corrosion
indicators
Environmental
indicators
Pipe deteriorates quickly in
more corrosive soil and the
degree of deteriorationdepends on the pipe material
Soil corrosivity
(Ohm)
The more permeable surface
allows more moisture to
percolate to the pipe. Thesurface salts will be carried to
the pipe with moisture
Surface
permeability
The water pipes aredeteriorated by thegroundwater table
Gouundwater
condition
Pipes buried at higher depth
have more possibility of
failure than those buried atshallower depths
Buried depth
(m)Load/strength
indicators;intermittency
indicators
Pipe failure rate increases
with traffic loadsTraffic load
Changes to internal waterpressure will change stressesacting on the pipe
Maximum
pressure
The more valves, the more is
the deterioration of the pipeNo. of valves
Operational
indicators
The more water supplies, themore the pipes will be
deteriorated
No. of watersupply/day
The more the duration ofwater supply, the less chances
of pipe failure
Duration ofwater
supply/day
The number of pipe breakages
per year
Breakage
history Failure indicator
Based on their similarities, the first-level indicators are aggregated to form the second-levelindicators. Similarly, the second-level pipe condition indicators are aggregated to form the final
indicator. Based on the hierarchical pipe condition structure established from the above
aggregation process, fuzzy composite programming ( [Bardossy and Duckstein,
1992]and[Bender and Simonovic, 2000]) is used to compute an indicator distance metric for
each indicator, and finally an overall distance metric is obtained using Eq.(4).
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(4)
whereLj is the distance metric of pipej, wj,i is the weighting of indicator i in groupj,pj is a
balance factor among the indicators for groupj,fbj,j is the best value for indicator i in groupj,fwj,i
is the worst value for indicator i in groupj, andfj,i is the actual value for indicator i in groupj.
The weightings are assigned to indicate the relative importance of the various pipe indicators in aparticular group. They are generated by following an analytical hierarchy process (AHP) (Saaty,
1980). Balance factors that determine the degree of compromise between indicators of the same
group are assigned to different groups.
The final overall distance metric is a fuzzy number represented by a membership function and is
defuzzified using the maximising and minimising sets (Chen, 1985). The defuzzified numbersthus rank the pipes according to their conditions. This metric will be used as a surrogate for the
vulnerability of the water distribution pipe (Eq.5). The details can be found inYan and
Vairavamoorthy (2003).
(5)
where VUk is the vulnerability of the water distribution pipe k, df is the method for
defuzzification and TFk is the trapezoidal fuzzy number for pipe k.
2.3. Risk assessment
The risk of contaminant intrusion into the water distribution system results from the interaction
between the hazard agent and the vulnerability of the water distribution pipe. The riskassessment model calculates this risk of contaminant intrusion by combining the outputs from
the above two models.
In this study, the hazard agent is the contaminant loading along the section of pipes in the
contaminant zone. The contaminant loading is obtained from the length of the section of pipe in
the contaminant zone and the concentration of contaminants along this section of pipe (Eq. (6)).As stated earlier, these parameters (SPCZ and the contaminant concentration) are obtained with
the help of the contaminant ingress model.
(6)
where HAk is the estimation of hazard for pipe k, LCkis the normalised length of the polluted
pipe k, obtained from Eq.(1),rk is the normalised radius of pipe kand CCkis the normalisedcontaminant concentration of pipe k, obtained from Eq.(3).
The condition of the water distribution pipe, which is calculated with the help of the pipecondition assessment model (Eq.(5)), is used as a surrogate of the water pipe's vulnerability to
intrusion.
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The combination of hazard agent and the vulnerability of the water pipe produces the risk index
for contaminant intrusion into the water distribution system. This paper uses a conventionalmultiple criteria evaluation (MCE), the weighted linear combination (WLC), to calculate the risk
index.
(7)
where RI is the risk index, wh is the weighting for hazard agent, wv is the weighting for thevulnerability of the water pipe, VUk is the vulnerability of the water pipe, obtained from Eq.(5)
and HAk is obtained from Eq.(6).
3. Software development
The three models were developed in the C++ programming language and integrated with
ArcView 3.2 GIS, using a tight-coupling approach (achieved with the Avenue programminglanguage and dynamic link libraries (DLLs)). This GIS-based tool is the spatial decision support
system, which has been named as the Improved Risk Assessment-Water Distribution System(IRA-WDS).
ArcView Dialog Designer has been used to design the graphical user interfaces that enable
seamless interaction of the user with the models developed. The interface allows the input andretrieval of data to and/or from the model in a user-friendly way. The GUI also provides the
functionalities for generating input files, running external models and converting and displaying
model outputs from formats such as ASCII files to thematic maps.
The section below describes the main components of the software. The software includes several
modules based on mathematical models and a GIS-based interface.
3.1. Contaminant ingress model
The contaminant ingress model consists of three modules: the contaminant zone, contaminantseepage, and contaminant transport. The data required for this model includes water distribution
system data, pollutionsource data (sewer, canal, and foul water bodies) and soil data. The details
of modules are described inTable 2.
Table 2. Module descriptions for contaminant ingress model
Module Purpose Input Output
Contaminant
zone
To determine the contaminant
zone of pollutionsources andSPCZ along water distribution
pipe. SPCZ is one of the risk
factors in risk assessment model
Networks data for
pollutionsources and
water distributionsystem
SPCZ along
water
distribution pipe(m)
Contaminantseepage
To calculate flow velocity due tothe pollutionsources. The flow
Properties for soil andpollutionsources
Pore-watervelocity (cm/h)
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Module Purpose Input Output
velocity is used for contaminanttransport model
Contaminanttransport
To calculate contaminant
concentration above water
distribution system. Theconcentration is another factors in
risk assessment model
Properties for soil andpollutionsources
Relative
contaminantconcentration
The contaminant zone module predicts the contaminant zone for a particular pollutionsource
(sewers, open drains and foul water bodies). The SPCZ for the water distribution system is thenidentified using spatial analysis involving contaminant zones and water distribution pipes.
The contaminant seepage module predicts flow velocity for both saturated and unsaturatedflows, using Darcy's law and the Green Ampt model respectively. The flow velocities are inputs
for the contaminant transport module.
The contaminant transport module calculates the contaminant concentration along the SPCZ for
steady and unsteady state conditions. These outputs represent the hazard factors in the risk
assessment model.
3.2. Pipe condition assessment model
The pipe condition assessment model consists of five modules: the fuzzy calculator; criteria
normalisation; the weighting generator; the fuzzy composite programme; and the classification
module. The data required for the pipe condition assessment model concerns the water
distribution system, the pipe deterioration indicator along with groundwater and pressure zonedata. The details of these modules are described inTable 3.
Table 3. Module descriptions for pipe condition assessment model
Module Purpose Input Output
Fuzzy
calculatorTo perform fuzzy arithmetic
Fuzzy membership
functions
Aggregated fuzzy
number
Criteria
normalisation
To normalise deterioration
factors to the same scale and
used for composite
programming
Properties for soil
and pollutionsources
Normalised
criteria for
composite
programming
Weight
generator
To generate weights for
deterioration factors usingeither analytical hieratical
process (AHP) or assign
weight directly and balance
factors
Pair-wise
comparison from
interviewing experts
Weight and
balance factor
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Module Purpose Input Output
Fuzzycomposite
programming
To calculate condition index
using deterioration indicators
Pipe deteriorationdata for water
distribution system
Pipe condition
index
Ranking andclassification
Rank pipe condition index that
are fuzzy numbers and classifyinto groups given by user
Pipe condition index
from compositeprogramming
Condition groupclassifications
Thefuzzy calculator module performs fuzzy arithmetic calculations. Both triangular and
trapezoidal fuzzy numbers are considered by this module. Various operators (e.g. addition,
subtraction, multiplication, division, power), are overloaded so that normal arithmetic operators
(+, , , /) can be used.
The criteria normalisation module normalises pipe deterioration factors using criteria A, B, C orD that are used in the composite programming.
The weighting generator module generates weightings and balance factors. The weights andbalance factors (used for pipe condition indictors and groups) are either assigned directly by the
user or generated using the analytical hieratical process (AHP). In addition, default weightings
obtained by interviewing engineers and practitioners working on network operations have beengiven.
Thefuzzy composite programming module aggregates the normalised deterioration indicators,weightings and balance factors to calculate a pipe condition index for each pipe. Each class in
this module performs this operation for a specific level of the composite hierarchy structure.
The ranking and classification module defuzzifies the fuzzy pipe-condition index, obtained from
the fuzzy composite programming module for each pipe and ranks/groups pipes according to
their defuzzified pipe-condition index.
3.3. Risk assessment model
The risk assessment model consists of four modules: risk factors normalisation; weightinggenerator; risk estimator and the classification module. The details of these modules are
described inTable 4.
Table 4. Module descriptions for risk assessment model
Module Purpose Input Output
Criteria
normalisation
To normalise risk factors to the
same scale and used for risk
estimator
Outputs from ingress
model and pipe
condition assessmentmodel
Normalised risk
factors
Weight
generator
To generate weights for risk
factors using either analytical
Pair-wise comparison
from interviewing
Weight for risk
factors
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Module Purpose Input Output
hieratical process (AHP) orassign weight directly
experts
Risk estimator
To generate risk index using
the risk factors obtained from
ingress model and pipecondition assessment model
Normalised risk factors Risk index
Ranking and
classification
Rank pipe risk index and
classify into groups given byuser
Risk index from risk
estimator
Risk group
classifications
The risk factor normalisation module normalises a particular risk factor. For example the SPCZ
and contaminant concentration risk factors are normalised and used to calculate the hazard. The
hazard and vulnerability risk factors are then normalised and used to assess risk.
The weighting generator module generates weightings for each of the risk factors. These areeither assigned directly by the user or generated using the analytical hieratical process (AHP).
The risk estimator module generates the risk index for each pipe by using a weighted linear
combination (WLC).
The ranking and classification module ranks and groups pipes according to their risk index (the
number of groups are specified by the user).
3.4. Integration into the GIS
The integration of environmental models with GIS has been discussed by several researchers ([Goodchild et al., 1992],[Goodchild et al., 1993],[Stuart and Stocks, 1993],[Batty and Xie,
1994],[Fotheringham and Rogerson, 1994],[Fischer et al., 1996],[Goodchild et al., 1996],
[Karimi and Houston, 1996],[Longley and Batty, 1996]and[Fotheringham and Wegener,2000]). In addition,Karimi and Houston (1996)andTait et al. (2004)also attempted to classify
different integration methodologies. In general, three methods of integration or coupling are used
to link environmental models with GIS. These are: loose coupling, tight coupling and embeddedcoupling. These three integration methods differ in their architectural characteristics and this
depends on the degree and form of data exchange or sharing between the GIS and the external
models ( [Goodchild et al., 1992],[Goodchild et al., 1993],[Nyerges, 1992]and[Fedra, 1993]).
With loose coupling, the GIS serves as both a pre-processor and a postprocessor to the modelling
system, while tight coupling is the integration of the models and GIS under a common interface.In embedded coupling, the models are developed within the GIS environment or alternatively a
GIS component is added to the modelling system (Huang and Jiang, 2002). Where complex
models of different domains are to be integrated with GIS, tight coupling is needed (Karimi andHouston, 1996). The tight coupling approach also offers full control to the experienced user and
minimal interaction to the novice. According toTait et al. (2004), through tight coupling,
modellers can spend more of their effort on building the models themselves. Therefore the tight
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coupling approach has been adopted in the development of IRA-WDS.Fig. 3illustrates the
integration of models with GIS.
Fig. 3. Integration of models with GIS.
The GUI enables the data to be retrieved from the spatial databases, and then passed to therequired models. The outputs from the models are then converted and presented as tables and
thematic maps. This is all achieved seamlessly through DLL calling functions within the GIS.
A Document Graphical User Interface (DocGUI) for IRA-WDS (Fig. 4) has been created based
on the View Document GUI of ArcView. Hence the GUI has similar functionalities to View
document, namely allowing the user to display, explore, query and analyze geographic data in
IRA-WDS.
Fig. 4. The major components of GIS-based IRA-WDS.
This IRA-WDS document GUI includes a selection of controls such as menus, buttons and tools
that can be used to interact within the IRA-WDS document GUI. The menus provide sub-menusfor generating and loading the input files, for executing the models (through DLL) and viewing
the model outputs in tabular or thematic map formats. The model outputs are stored in both
ASCII and shape file format (using Avenue script written in the IRA-WDS). The main menus inIRA-WDS launch various important dialog boxes:
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The data preparation menu launches dialog boxes (Fig. 5) that allow the user to convert
data from one format to another and to upload relevant shape files and tables into the
IRA-WDS platform.
Fig. 5. Addition of shape files through Data Preparationmenu.
The contaminant ingress menu launches dialog boxes (Fig. 6) that allow the uploading ofthe relevant shape-files for the model (i.e. water distribution network; sewer network;
canal network; surface water bodies and soil type (note that there is a default database of
soils)).
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Fig. 6. The main interface for Contaminant Ingress Model.
Thepipe condition assessment menu launches several dialogs boxes (Fig. 7) that allowuploading of the relevant shape-files for the model (i.e. water distribution system, soil
data, groundwater table and hydraulic pressure). The user can specify the pipe indicatorsto be considered for the condition assessment and their respective crisp/fuzzy values(fuzzy values are inputted by means of a suitable membership function). Note that there
is a default database for pipe materials. The user can specify the weightings and balance
factors for the pipe indicators (note this dialog box will launch another that will allow the
weightings to be generated by the AHP method) and a local or global analysis and theninput the range of values to be used (minimum and maximum). Note that there is a
default database for the ranges.
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Fig. 7. The main interface for Pipe Condition Assessment Model.
The risk assessment menu launches a dialog box (Fig. 8) that allows uploading of therelevant output files from the contaminant ingress and pipe condition assessment model.In addition it allows the user to specify the weights required for hazard and vulnerability
(note that this dialog box will launch another that will allow the weights to be generated
by the AHP method).
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Fig. 8. The main interface for Risk Assessment Model.
4. Application of IRA-WDSIRA-WDS was applied to one of the ten zones of the Guntur Municipal Corporation, Guntur,
Andhra Pradesh, India. This zone is the B. R. Stadium Zone and called Zone VIII. This zone
has a population of about 60,000 and an area of 4 km2. As in most parts of the country, an
intermittent water supply system is prevalent in this area. Water is supplied through pipe
networks for about 1 h per day.
The drinking water supplies in this area are prone to contamination due to the various
pollutionsources that exist in this zone. Therefore the authorities responsible for managing this
system are keen to understand the risks to the water supply associated with the hazards described
above and to develop a maintenance strategy that will provide maximum improvements to waterquality within their limited budget.
At several locations, underground sewer pipes run close, parallel and above the water
distribution pipes. Frequent leaks are reported in the sewer network due to blockages. Sewer
pipes only cover a small part of the case study area. Most sewage is directly discharged into opendrains and canals. Considerable amounts of seepage occur from these open drains and, since they
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are at ground level, this seepage can reach the water distribution pipes. Stagnant water is also
present in natural depressions in the study area.
4.1. Data collection
The application of IRA-WDS to the Guntur case study required the construction of severaldatabases. The details of the data requirements of IRA-WDS are given inTable 5. These
databases were constructed using data collected over a period of 12 months from June 2002 withthe assistance of the local authorities and a local NGO. Four thematic maps were constructed
related to: the water distribution network; the sewer network; the network of canals/open drains
and the surface water bodies (seeFig. 9).
Table 5. The data requirement of IRA-WDS
Water distribution
systems
Network map (link and node coordinates in metres) and for each
pipe of network: length of pipe, joint method, material type,
traffic load, surface type, internal protection, external protection,bedding condition, workmanship, diameter of pipe, installation
year, bury depth of start node, bury depth of start node, no of
connections, no of breaks per year, number of valves, duration ofwater supply per day (h/day), number of times water supplied per
day
Pollutionsources
The possible pollutionsources and the data needed for each
pollutionsources is described below. Underground sewer pipe:
network map (link and node coordinates), and for each pipe itslength, bury depth, material, leakage rate (m/day) and diameter.
Lined open ditch/drain: network map (link and node coordinates),
and for each ditch/drain of network, its length, material, depthand leakage rate. Unlined open ditch/drain: network map (link
and node coordinates), and for each ditch/drain of network, its
length, depth, soil type and seepage rate. Surface water bodies:surface water bodies map and for each surface water body, its
area, depth, soil type and seepage rate
Soil
Different soil types and for each soil type: saturated volumetricwater content, initial volumetric water content, saturatedhydraulic conductivity (cm/h), soil characteristic curve
coefficient, soil porosity, air entry head (cm), pore size index,
bulk density (g/cm3)and fraction organic content
Contaminant/pollutant
For each pollutionsource: liquid phase decay (/h), diffusion
coefficient (cm2/day), organic carbon partition coefficient of the
pollutant
Pipe material
Different types of pipe material used and for each type: corrosion
index, maximum pressure rating (kg/cm2), maximum load rating
(m-kg/m), design life, maximum diameter (mm), minimum
diameter (mm) and the variation of HazenWilliam coefficient
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(C) of friction with the age of pipe
Groundwater
Groundwater zones in the area and for each zone, average depth
to groundwater table (m) and average fluctuation of groundwater
(m)
Pressure zone
Pressure zones in the water distribution system network and for
each zone the pressure in the system
Fig. 9. Thematic maps for case study area in Guntur, India. (a) Water distribution system;
(b) sewer system; (c) canal/open drains system and (d) foul water bodies.
The three models of IRA-WDS (contaminant ingress, pipe condition assessment and risk
assessment model) extract the necessary information from the corresponding databases of eachtheme in order to run the simulations. The outputs of each model are returned back to the geo-
database where the corresponding output themes are generated.
In this study, three output themes are generated namely: the SPCZ theme generated from the
ingress model; the pipe condition theme generated from the pipe condition assessment model and
the risk assessment theme generated from the risk assessment model.
4.2. Results
The outputs of IRA-WDS (SPCZ, pipe condition and risk maps) for the Guntur case study areaare discussed in this section.
4.2.1. SPCZ Map
The SPCZ map shown inFig. 10, indicates the section of pipes in the water distribution system
that are within the contaminant zone of pollutionsources (sewers, canals/open drains and foulwater bodies). In addition, the relative contaminant loading on the SPCZ is also shown.
Therefore this output gives an indication of the sections of water pipes in danger of being
contaminated.
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Fig. 11. Water pipe condition map for Guntur case study area.
4.2.3. Risk map for contaminant intrusion
The risk map generated for the Guntur case study area is shown inFig. 12. This figure indicates
that most of the water pipes have a medium or low risk. However, a small number of pipes havea high risk of contamination. From the risk map inFig. 12, several recommendations were made
to reduce the risk of contaminant intrusion. These suggested that the authorities should:
replace/rehabilitate AC pipes which are found to be in bad condition and hence have avery high susceptibility to contaminant intrusion (e.g. risk area A).
undertake a leakage detection and repair programme in areas with many joints andconnections (e.g. risk area B);
inspect open drains and reline where necessary (e.g. risk area C);
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provide protection to water pipes in areas where they are close to the open drains (risk
areas D);
de-water and fill foul water bodies in the north east (e.g. risk area E).
Fig. 12. Risk map for contaminant intrusion for Guntur case study area.
Though the results obtained for the case study area could not be validated with actual field data,
nevertheless the IRA-WDS provides engineers with valuable insights into the risk of watercontamination in a water distribution system. One of the major benefits of using IRA-WDS, is
that it is possible for the decision makers to gauge the impacts of the above recommendations on
the risk index. This can be achieved by simply modifying the database appropriately andrerunning the model. However, as there may be several other objectives related to an investmentstrategy for the water distribution system, these should also be considered. For example, it would
be prudent to combine the outputs of this model with a hydraulic model to establish the most
significant pipes in terms of both the risk of contaminant intrusion and changes required toimprove the hydraulic carrying capacity of the system.
5. Conclusions
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This paper recognises the need of municipal engineers in developing countries for a tool to
assess the risk of contaminant intrusion into a water distribution system so as to prioritise riskmitigation actions. Currently there are no tools available to enable engineers to identify the risks
associated with contaminant intrusion into intermittent water distribution systems. This research
has identified a sourcepathwayreceptor approach for establishing the risk of contaminant
intrusion and developed a powerful tool, IRA-WDS for this purpose. This tool enables decision-makers to prioritise their investments in relation to water quality management.
IRA-WDS consists of three separate models. The contaminant ingress model establishes the
hazard by estimating the contaminant loading on a section of water distribution pipes passing
through a contaminant zone. The pipe condition assessment model establishes the vulnerabilityby estimating the relative condition of pipes using fuzzy composite programming techniques.
The risk assessment model then calculates risk by combining the outputs from the above two
models (hazard and vulnerability).
The three models were developed in the C++ programming language and integrated with GIS
using a tight-coupling approach (achieved with the Avenue programming language and dynamiclink libraries (DLLs)). IRA-WDS also has a powerful GUI that enables data for the models to beretrieved from the spatial databases and the outputs from the models to be converted and
presented as tables and thematic maps. This is all achieved seamlessly through DLL calling
functions within the GIS.
IRA-WDS has been applied to a water distribution network in south India and the outputs used to
generate several thematic maps. These maps identified sections of the system that were most atrisk and also provided an understanding of the main factors that contributed to the risk. The
outcome of using this tool enables engineers to prioritise maintenance for risk mitigation and
also provides decision-makers with a better understanding of the process of contaminant
intrusion.
It is anticipated that IRA-WDS will find wide application among water utilities, especially in
developing countries where intermittent water supplies are the norm.
References