Optimization for Location of Sensors in Water Distribution for
Contaminant Detection (Rawal Dam Watershed)
(Naveed Mustafa* and Dr. Bashir Ahmed**)
Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Islamabad. (SZABIST)
Islamabad, Pakistan
*
**
PhD Student at SZABIST, Islamabad
Supervisor, Principal Scientific Officer at Pakistan Agricultural Research Council, Islamabad.
Correspondence Authors:
Naveed Mustafa,
Scientific Officer (Computer Modeling)
Water Resources Research Institute,
National Agricultural Research Center,
Park Road, Chack Shahzad,
Islamabad.
E Mail: rana_naveedmustafa@yahoo.com
Mobile: 0301-5413238 Off. 051- 8443646
Res.
051-2252840 Fax: 051- 9255074
Dr. Bashir Ahmad,
Senior Scientific Officer,
Water Resources Research Institute,
National Agricultural Research Center,
Park Road, Chack Shahzad,
Islamabad.
E Mail: bashirad@hotmail.com
Mobile: 0301-5413238 Off. 051- 8443646
Res.
051-2252840 Fax: 051- 9255074
Optimization for Location of Sensors in Water Distribution for
Contaminant Detection (Rawal Dam Watershed)
ABSTRACT;
The fresh drinking water is basic need of every human being. With the increase in population the dependency on
water resources increasing day by day which are all ready in scared. But the population expands on the banks of
rivers, nalas, and lakes or on water ponds. Because in this they can easily access water and there sewerage enters
in these water bodies. In this way the lakes and water resources polluted which are dangerous for the health of
downstream population. The Rawal Dam is a key water supply resource for citizens of Rawalpindi and cantonment.
The increasing population nearby of this dam such as Bari Imam, Noor pur shahan, Banigala, diplomatic Enclave
and Quiad-e-Azam University contributing a lot of contamination in the dam. The increasing in the technology
there are a number of new and advanced system which can be used for detection of contamination and alarming
systems to alarm about any contamination before it happened. There are a number of different types of sensors
which are using for detection of contamination. The use of GIS (Geographic Information System) with sensors to
detect about exact location and analysis that how much it effects can easily perform. In this paper the focus area is
that how different sensors which are used for detection of different sensors can be integrated to detect different
contaminations for water resources or water distribution channels.
General Terms: Geographic Information System (GIS)
Keywords:
service-oriented architecture (SOA), Remote Sensing (RS), Sensors, GPS (Global Position
System), Relational Database, watershed, sensors ontology, TDS (Total Dissolved Solids), CWS
(contamination water systems), SPOT (Sensor Placement Optimization Toolkit)
1.
INTRODUCTION;
About 14, 000 people died daily due to water pollution in the world [1]. According to 2nd UN
World Water Development Report that a billion people which are more than one-fifth of the total
populations are away from safe dirking water.
Geographic information system (GIS) have wide-spread utility in a variety of domains for the
management of complex data obtained from Remote Sensing, automated mapping and facilities
management systems, and myriad of other applications. Recently, some researchers are applying GIS to
manage the placement of wired and wireless sensors and sensor networks across large, and often remote
geographic regions, as wells developing GIS interfaces to dynamically discover, query and task sensors
within a service-oriented architecture (SOA) [2].There are two types of sensing equipment (devices),
which are stand alone sensors and sensors within network. Sensor network composed of node of the more
sensing devices on the node stand alone sensors perform functions autonomously and don not rely on
other senores, many stand alone devices have a programmer’s interface (API) to task and received data
from the snores stand alone sensors can be integrated into a network, but are not regards as nodes that
from a traditional sensor network. Most of APIs required detailed programming knowledge in a language,
such as C, while a few of PIs re being to provide higher level programming abstractions, which are
common in service-oriented computing paradigm[2]. The sensors may be included information such as
temperature, light intensity, GPS points and Geographic Locations. Senores network grow rapidly and
improve their ability to measure real time information in an accurate and reliable fashion, how to collect
and analyzed this huge generated information [3].Near real time continuous monitoring systems have
been proposed as a promising approach for helping drinking water utilities detect and respond quickly to
threat related to the normal operation of water the water network. Water quality sensors may detect
contamination events that pose a growing threat to public health [4].
2.
BACKGROUND/ LITERATURE REVIEW;
The Contamination means that undiluted and dangerous to health elements with eatables/drinkable water.
Water has biological and no-biological contamination. The cause of contamination such as increase in
population, industrialization, domestic waste of houses (chemical detergents), and human excretes, animal
excretes. There is natural contamination in water resources such as soil, rocks dissolving. The water
contamination has different types of components/ test such as Water Temp. ºC, Field Temp. ºC, pH, DO
mg/ℓ, DO mg/ℓ, BOD mg/ℓ and COD mg/ℓ.
The contamination water causes diseases by pathogenic bacteria, viruses and protozoan parasites. The
water which is contaminated trough water drops, aerosols and washing or bathing. The life threaten
disease caused by these pathogenic Microorganisms are typhoid fever, cholera Hepatitis A or E. in
Pakistan the Hepatitis increasing day by day. Some survey reports that every 12th person in Pakistan is a
patent of Hepatitis. The main cause of its spreading in Pakistan is use of contaminated water. Because
industrial, domestic, agricultural wastes are adding in the rivers and canal without treating it, it is causing
a serious threat to the lives of Pakistanis.
Geographic information system (GIS) have wide-spread utility in a variety of domains for the
management of complex data obtained from Remote Sensing, automated mapping and facilities
management systems, and myriad of other applications. Recently, some researchers are applying GIS to
manage the placement of wired and wireless sensors and sensor networks across large, and often remote
geographic regions, as wells developing GIS interfaces to dynamically discover, query and task sensors
within a service-oriented architecture (SOA) [2].
There are two types of sensing equipment (devices) which are stand-alone sensors and sensors within
network. Sensor network composed of node of the more sensing devices on the node stand-alone sensors
perform functions autonomously and don not rely on other sensors, many stand-alone devices have a
programmer’s interface (API) to task and received data from the sensors stand-alone sensors can be
integrated into a network, but are not regards as nodes that from a traditional sensor network. Most of
APIs required detailed programming knowledge in a language, such as C, while a few of PIs re being to
give higher level programming abstractions, which are common in service-oriented computing model[2].
The sensors may be included information such as temperature, light intensity, GPS points and Geographic
Locations. Sensors network grow rapidly and improve their ability to measure real-time information in an
exact and reliable fashion, how to collect and analyzed this huge generated information [3].Near- real
time continuous monitoring systems have been proposed as a promising approach for helping drinking
water utilities detect and respond quickly to threat related to the normal operation of water the water
network. Water quality sensors may detect contamination events that pose a growing threat to public
health [4].A multi-objective frame work for sensors layout design was suggested by [21] in which the
ideal locations are determined with the aim of collecting data that will be used later in the calibration of
analyzed water system hydraulic model. The problem is formulated as two objective optimization
problem involving maximization of the calibrated model accuracy by minimization of the relevant
uncertainties and local cots [21, 6].
The first who has introduced a multi-objective formulation to sensors placement by employing a mixed
integer linear programming model over a range of design goals [21].The battle of the water sensors [22]
highlighted the multi-objective of the nature sensors placement, comparing different multi-objective
optimization models [23].
A wide range of sensors placement optimization formulations and solver techniques have developed for
CWS design in drinking water systems [2, 24, 25, 26, and 27].
H. Griffiths briefly explained about the modeling aspects of water contamination in water resources of
drinking water. He also briefly explained about the increasing threat of terrorist attacks of water storages
after 9/11. The terrorist attacks on these storages can be a serious loss for life and buildings. Therefore
CBRN (Chemical Biological, Radiological and Nuclear).The applications of CBRN modeling such as for
Hazards Predictions, Information Systems, Operation Analysis and Modeling and simulations [28].
Artificial intelligence (AI) is the scientific understanding of the mechanics underlying thought and
intelligence behavior and their embodiment in machines [2, 27]. Artificial concept like search, planning,
natural language and so on has an important role on expert system of decision system [6, 29].The decision
system of determination contamination detection systems has pattern recognition pattern, ontology,
ontology modeling, and intelligent rules. The ES (Expert System) or EDSS (Expert Decision Support
System) with unique functionality, supervisory control and acquisition [29, 30]
The proper use of sensors/sensor network in an ES would be able to combine all early warning
information system from source of water monitoring systems to process monitoring of water treatment
plants, and also monitoring of water quality in distribution systems [6].
Instrument selection (Selection of sensors) is accomplished by screening of all current and emerging
sensors which are available. The sensors selection criteria includes of fast response time, easy handling,
protected, adaptable, alarm and control features, low-cost [6, 10],
The combination of ‘field graded” water quality motoring sensors and software with intelligent agents
can produce relatively low-cost monitoring and reliable information about the behavior and
contamination of water in it [6].
MYCIN can be applied to build up of ES system and that system can attached with GIS to delineate of the
environment and water system with it [6]. For this purpose the open GSI standers can be used. The expert
system with small- scale water distribution channels cab is easily applied and produced results as compare
to the any other systems. The expert system is simple and there is no need of costly hardware and
software.
The term watershed refers to the geographic boundaries of a particular water body, its ecosystem and the
land that drains to it. A watershed also includes groundwater aquifers that discharge to and receive
discharge from streams, wetlands, ponds, and lakes [10]. Large watersheds are sometimes referred to as
river basins. Rawal Dam Water Shed consist of are four major streams and 43 small streams. These 43
small and four major streams contribute water in Rawal Dam.
3.
PROBLEM FORMULATION;
Fresh and clean water is life of all living organisms including human beings. With explosive
increase in population the demands of water for drinking and domestic purpose increasing day by day.
Islamabad and Rawalpindi are two most important and key cities of Pakistan because of Rawalpindi is
Military General Headquarters of Pakistan army and Islamabad is capital of Pakistan. Rawal & simly
dams are two key sources of water supply to these twin cities. Rawal Lake is the main source of water
supply for Rawalpindi city and cantonment. It has been constructed on Korang River and has a catchment
area of 106 square miles, which generates 84,000 acre feet of water in an average rainfall year. There are
four major streams and 43 small streams contributing to its storage. Chattar and Lake View recreational
points along with Villages of Bhara Kahu, Malpur, Banigala, Noorpur Shahan and Quaid-e-Azam
University contributing contamination in Korang and Bari Imam nullahs which enters in the Rawal Lake
as shown in fig 1.
Fig.1settelments contributing contamination along with Rivers in Rawal Dam
The Rawal Dam has all type of contaminations such as human induced and natural. The untreated
sewerage water from nearby villages has human and animal excreta.
The chemicals contaminations which also added in the dam water produced by nearby villagers using
detergents and soaps etc. the biological micro-organisms such as bacteria, allege and other are also found
in the Rawal Dam.
The Rawal Dam water has pH 7.9 and 24 Turbidity (NTU). Other contamination values are in Table. 1.
1
pH
2
Turbidity (NTU)
7.9
24
3
TDS (mg/l)
4
Coliform (MPN/100ml
5
E.Coli (MPN/100ml)
208
> 16
> 16
Table: 1 Source: Pakistan Council of Research in Water Resources (PCRWR)
There are samples taken from different points for contaminations indication water. The points from which
samples collected are in table 2.
Sample
Sample site
RD-1
Rawal Dam (around the inlet from Kurang river)
RD-2
Rawal Dam (center of Rawal Dam)
RD-3
Rawal Dam (around the outlet for Kurang river)
RD-4
River (stream at Bara Kahu before entering river)
RD-5
River (stream near junction Islamabad Murree Road)
RD-6
River (stream from Diplomatic Enclave before entering)
WRP
Clean water from Rawal filtration plant
Table: 2 Source Pakistan Environmental Protection Agency
The samples taken from Kurang River inlet, the point form where water enters in the Rawal Dam has pH
value of 8.4 which is greater than 7 from normal and moves towards basic sides. Other values of
contamination which is in Kurang River are Water temperature, field temperature, DO, BOD, COD has
values 24.5, 30.3, 7.8, 2.1 and 9 respectively. Other sample points such as a point taken from the center of
Rawal Dam and other sample sites in table.1 and there test values are present in table3 [9].
Field Temp. ºC
pH
DO mg/ℓ
BOD mg/ℓ
COD mg/ℓ
30.3
8.4
7.8
2.1
9
RD-2
24.3
30.1
8.4
RD-2(2m)
15.2
18.4
8.0
RD-2(11m)
14.8
18.5
8.0
RD-3
26.0
30.1
8.4
WRP
15.5
26.6
7.0
Table: 3 Source Pakistan Environmental Protection Agency
7.0
8.5
9.6
7.3
6.8
2.2
2.2
2.4
1.9
0.1
10
4
6
15
4
Sampling
Points
RD-1
Water
Temp. ºC
24.5
4.
METHODOLOGY;
4.1
Sensor System Architecture with GIS;
The prototype GIS system architecture [2], ESRI’s Arc GIS has capacity to do GIS platform. The figure 1,
which satellite image of Google earth Rawal Dam with its other shows the points where contamination in
dam occurs.
The sensor’s ontology has knowledge about a variety of wired and wireless sensors, but water
contamination sensors can be placed instead of other networks. For water contamination detection, there
can be used biological factors detectors and non-biological contaminations detectors. The biological
contamination is micro-organism growth in water resources such as dams, nalas, rivers and ponds. The
micro-organisms are such as Bacteria, protozoan, algae etc.
The non-biological contamination is different types. The non-biological contamination consists of
chemical and non chemical. The chemical non-biological contamination consists of industrial waste,
rocks destruction and detergents use in houses. The non-chemical contamination is human and animals
excretion.
The sensors which are used to detect biological contamination are Turbidity, E.Coli. The sensors which
are used to non-biological contaminations detection such as pH, TDS (mg/l).
4.2
Prototype Architecture of Sensor Deployment with GIS;
The prototype Architecture of sensor deployment with GIS consists of following components.
These components are:
Relational Database, Sensor ontology, GIS layer, Standalone Sensors and Sensors Network [2]. The GIS
tools used for visualization of data in form o f maps of the location.
Fig. 2 Prototype Architecture of Sensor Deployment with GIS [2]
The sensor ontology is repository of sensing devices, metadata about senores and sensing devices and
relationships between different sensing devices [2, 7, and 8]. The senor’s development is an ongoing
process as new sensors are developing day by day. The relational database
4.3
Formation of Water Contamination Ontology (A collection of sensors);
There different types of sensors form and work together are formation of ontology. To detection of
contamination from water, there are a number of types of sensors. Some sensors brief explanation in fig.4.
The contents of pH in water mean values of concentration of hydrogen ions, which tells us about the
amount of acidic and basic. The pH values range from 1 and 1x 10-14 grams equivalent per liter into
numbers 0 to 14. The value of 7 is a neutral solution. The greater than 8 values indicate basic
Fig. 3 pH value indicator scale
Contents in water and less than 7 show acidic. The value 6 indicates the water contains more acidic has in
it as shown in fig. 2. The (pH Senor) pH measuring sensors consists of three such as electrode, a reference
electrode, and a temperature sensor; a preamplifier; and an analyzer or transmitter. The pH of pure water
is 7.
Turbidity Sensor is a 90 degree scatter nephelometer. The sensor directs a focused beam into the
monitored water. The light beam reflects off particles in the water, and the resultant light intensity is
measured by a photodetector positioned at 90 degrees to the light beam. The detected light intensity is
directly proportional to the turbidity of the water. The turbidity sensor utilizes a second light detector to
correct for light intensity variations, color changes, and minor lens fouling [15].
TDS, "Dissolved solids" refer to any minerals, salts, metals, captions or anions dissolved in water. This
includes anything present in water other than the pure water (H20) molecule and suspended solids.
(Suspended solids are any particles/substances that are neither dissolved nor settled in the water, such as
wood pulp.). The EPA Secondary Regulations advise a maximum contamination level (MCL) of
500mg/liter (500 parts per million (ppm)) for TDS.
1
2
3
pH
Turbidity (NTU)
TDS (mg/l)
4
5
Coliform (MPN/100ml
E.Coli (MPN/100ml)
Fig. 4 sensors
Numerous water supplies exceed this level. When TDS levels exceed 1000mg/L it is generally considered
unfit for human consumption. There is TDS detector sensors. Coliforms are a broad class of bacteria found
in our environment, including the feces of man and other warm-blooded animals. The presence of
coliform bacteria in drinking water may indicate a possible presence of harmful, disease-causing
organisms [29]. Escherichia coli (commonly abbreviated E. coli and named after its discoverer), is a
Gram negative rod-shaped bacterium that is commonly found in the lower intestine of warm-blooded
organisms. Most E. coli strains are harmless, but some, such as serotype O157:H7, can cause serious food
poisoning in humans, and are occasionally responsible for product recalls. The harmless strains are part of
the normal flora of the gut, and can benefit their hosts by producing vitamin K2 and by preventing the
establishment of pathogenic bacteria within the intestine [30, 31].
4.4
Sensors Installation Sites;
The installation sites of sensors are selected by using different techniques. But before going to select the
site, it has been known about the application of sensors and there out come. The sensors can be used to
detect contamination before filtration and after filtration. They can be installed at the points where water
tanks. But in this study the sensors points are suggested at the Rawal Dam where four main rivers enters
in the Dam. Because at these points a lot of quantity contamination enters into the dam. The points Map
of where Sensors may be installed are shown in fig. 5. The contamination at these points is important and
necessary.
There sensors should be applied after filtration to check the how much contamination removed from the
contaminated water of the Rawal Dam.
Fig.5 Sensors Installation Sites
4.5
Purpose of Sensors Installation;
The purpose of installation of sensors is to detect contamination from water, which are dangerous
for human and animal’s health and also for agriculture. The toxic elements which are present in water are
dangerous for humans, but these toxic materials dangerous for plants also. The sensors can be used for
Emergency Response Plan. The sensors can help Vulnerability Assessment. The Vulnerability
Assessment helps us for Prevention, Mitigation and for Recovery plan.
5.
CONCLUSION;
The uses of sensors are increasing day by day. Independent sensors are responsible to detect of
contamination which was it is specific for Example pH calculating sensors determine the pH quantity in
water like that Coliform checks micro-organisms growth in water which are dangerous to humans and
animals.
As prototype architecture with GIS [2] presented, this type prototype model used for detection of water
contamination from water resources is helpful.
In Pakistan there is every 12th person is a patient of Hepatitis. The sensors should be installed at all water
resources. The Rawal Dam is a major o water supply to Rawalpindi. In this we should be able to protect
from it is to be polluted. The GIS help our sensors to remains with a specific location. This type of
architecture in future cab applied at the public buildings such as hospitals, collages, universities, schools,
offices and Railway airport and bus stations. The GIS provides a multi-functional prototype, which helps
in deployment of sensors. The model has a decision system which involves activities such queering,
searching.
6.
FUTURE WORK;
At this I am only in position to propose a model. This model can be implemented. If this model is
successfully implemented. It can save a lot of time, price and human health. In future it can be used for
detection of under contamination water. In Pakistan use of sensors for this purpose in least. There is need
to use these sensors for this purpose to save life and their wealth which is expenditure for their health.
The sensors have following important uses.
Sensors can be used to detect pathogens and nutrients of ground water potentials.
In villages sensors are installed in the places where sewerage of water of villages is collected. The ponds
of sewerage water in villages polluting underground water. There is need to install these to save lives.
7.
ACKNOWLEDGEMENT;
The assistance of GIS team members of Geo-Informatics Lab of Water Resources Research Institute
National Agricultural Research Center, in digitization of thematic maps and database development for
this study is acknowledged.
8.
1
REFERENCES;
West, Larry (March 26, 2006). "World Water Day: A Billion People Worldwide Lack Safe
Drinking Water” Ian Zhao. Look, it's not there. Byte, 1:401-407, January 1997.
2
David j. Russomanno and Jury Tritenko (2010). “A Geographic Information System Frame work
for the Management of Sensor deployments”, 10, 428-4295; doi: 1010.3390/s100504281.
3
Sing M.P.; Huns M.N. Service Oriented Computing; John wiley & sons: west Sussex, UK 2005; pp.
19-42.
4
Xingchen Chu, Tom Kobilalka, Bohdan Durnota and Rajkumar Buyya “Open Sensor web
Architecture: Core Services”.
5
Ami Peris, Andrew Whittle and Avi Osfeld “Multi-Objective Sensors Network Placement Model
for Integrated Monitoring of Hydraulic and Water Quality Parameters”.
6
Chang, N.B and Makkeasorn, A. “Rule-based Expert System for Sensor Development in Drinking
Water System for Rural Communities”.
7
Russomanno, D.J Kothari, C.: Thomas, O. “building a Sensor ontology: a practical approach
leveraging ISO and OGC Models” in the proceeding of international conference on Artificial
intelligence, La Vegas, NV, USA, 2005.
8
Goodwin, J.C; Russomanno, D.J “ontomology integration within a service-oriented architecture for
expert system application using sensor networks” Expert syst. 2009.
9
Pakistan Environmental Protection Agency, Central Laboratory for Environmental Analysis and
Networking (CLEAN), http://www.environment.gov.pk/Laboratory.htm
10 Cook, J.B and Byrne, J.F., Daamen, R.C and Rehl Jr., E.A (2006): Distribution System
Monitoring Research at Charleston Water System” the 8th Annual Water Distribution Analysis
Symposium, cincimanti, Ohio August. 27-30, 2006.
11 http://www.wallkillriver.org/what_is_watershed.htm
12 http://www.dcu.ie/~best/st.htm
13 Jesse Zuehlke “Rapid Detection of food Born Ecolio157:47 using piezoelectric–excited Millimeter
size Ca.ntilever Sensors”.
14 http:// www.sensorland.com/HowPage037.htm
15 http:// www.envcoglobal.com/catalog/ product/digital-turbidity- sensors/wq720-turbidity-sensor.html
16 Goggle Earth (www.googleearth.com).
17 I.hyword, S. Cordius and S.Carver, “An Introduction to Geographic information System.” 2nd
Edition.
18 Bray, T., J. Paoli, C. M. Sperberg-McQueen, and E. Maler, cited. 2000: Extensible markup
language (XML). World Wide Web Consortium Tech. Rep. [Available online at http:
//www.w3.org/TR/REC-xml.].
19 http://www.informationbuilders.com/decision-support-systems-dss.html
20 www.google.com
21 De Schaetzen, w., walkers, G.A., and Savic, D.A. (2000). “Optimal Sampling design for model
calibration using shortest path, genetic, and entropy algorithm.” Urban water, 2, 141-152.
22 Ostfeld, Z. Savic, D.A., and Walters, G. A 92003). “Multiobjectice sampling design for water
distribution model calibration” J.Water. Plann. Manage.
23 Osrfeld A. et al. (+34 Co-authors) (2008). “The battle of the water sensors networks: a design
challenges for engineers and algorithms” Journal of water Resources Planning and Management
Division, ASCE, Vol. 134, No.6.
24 William E. Hart, Jonathan W. Berry and R. Murray “SPOT: A sensor Placement Optimization
Toolkit for Drinking Water Contamination Warning System Design” 2009.
25 J. Eckstein, J.E Upton, and C.A Phillips PICO “An object Oriented frame work for parallel branch
and bound” Elsevier science Publishers.
26 B.H Lee and E. Salmons “Optimally out of early warming detection stations for water distribution
systems security “journal of Water Resources Planning and Management, 2004.
27 J.P Watson, H.J Greenberg, and W.E Hart “A multi-objective analysis of sensors Placement of
optimization in water networks” in the proceedings of the world water and Environment Resources
Conference, 2004.
28 http://dvice.com/pics/PANTHER-pathogen-scanner.jpg
29 CDC Division of Bacterial and Mycotic Diseases.
http://www.cdc.gov/ncidod/dbmd/diseaseinfo/escherichiacoli_g.htm. Retrieved 2007-01-25.
30 Hudault S, Guignot J, Servin AL (July 2001). "Escherichia coli strains colonizing the
gastrointestinal tract protect germfree mice against Salmonella typhimurium infection". Gut 49 (1):
47–55. doi:10.1136/gut.49.1.47. PMID 11413110.
31 P, Weagant S, Grant, M (2002-09-01). "Enumeration of Escherichia coli and the Coliform Bacteria".
Bacteriological Analytical Manual (8th Ed.). FDA/Center for Food Safety & Applied Nutrition.
http://www.cfsan.fda.gov/~ebam/bam-4.html. Retrieved 2007-01-25.