Home Articles Optimum management of water and wastewater network in GIS environment using geospatial...

Optimum management of water and wastewater network in GIS environment using geospatial database, a case study on part of Ahvaz city, SW Iran

K. Rngzan*, A. Mehrabi*, R. Shad**, E. Abshirini*, M. Moradzadeh*
*Department of RSGIS, Faculty of science, Shahid Cahmran University, Ahvaz-Iran
Email: [email protected] – Fax: 06113339338
**Department of GIS, Faculty of Geomatic, KNTU, Tehran Iran

Nowadays, establishing a geospatial database is commonly the first step for managing the urban services. The aim of this paper is designing and implementing a cost-effective kind of geospatial database for managing water and wastewater networks. At first, it was essential to detect and remove different kinds of possible errors like; spatial, temporal and etc. Then, complete conceptual diagram is drawn for modeling and implementing geometric network in spatial database. After that, the geometric network designed for modeling flow is weighted based on transferring water volume, scale of pressure and the pump strength. Furthermore, customer’s demand is considered as a driven defined weight, for GIS analysis function. The wastewaters, in the geometric network are weighted using pipe’s diameters because of ground gradient. Finally, spatial networks functions are implemented together for optimum managing network. The final results demonstrate high ability of geospatial database for managing water and wastewater networks.

1. Introduction
Utilities are the important parts of cities extension. Water and Wastewater networks plays principle role in extending different kinds of civil applications like; residential, commercial, economical and etc. This fact along with increasing the price and the environmental disturbance of water shortage indicate that why relevant companies and offices completely pay attentions to optimum exploitation of resources. GIS as a spatial information system allows simple and comprehensive tools for optimum exploiting of water and wastewater networks. Civil water and wastewater networks witch are made with high costs are in basement. In the other word, all of water and wastewater companies are earth-bed. So, most of them explore modern exploitation methods like GIS to optimum management. Most primary works in this field are concentrated a preparation of database for relevant systems and services. This databases which should be work geospatially, must be accompanied by network designing and before burial of network. Many workers have shown the ability of GIS in urban water and wastewater net work (Nobel, (1998); Ramirez (1997); Bahadur et al. (2000); De schaetzen and Boulos (2001); Nielsen (2005); Rangzan and Mehrabi, (2007)). Goal of their research was developing database and transferring information between database and water and wastewater hydraulic model. Mathiyalagan, (2004) developed an interactive WebGIS and geo-database for Florida’s wetland. Their web-based tools facilities to share data globally provide end-user a cost-saving solution to access up-to-date spatial datasets customized foe specific topic to user with limited GIS knowledge. Also Bonniface and Coppins (2007) used geospatial database in their research.

In this investigation, a database is established for water and wastewater network of two civil zones of Kianpars and Kianabad of Ahvaz city. This base has a projected coordinate system for keeping data with high quality and fully structured topology. It also has the ability of establishing validation rule for data management of inner users can control editing data by defining these rules and recognizing contrary and prevent entering them. A network model in the form of geometric network has useful analysis for helping and accelerating verification networks.

Study area
This zone is located in North West of Ahvaz (between 31° 20´ 9´´ and 31° 22´ 11´´ latitude and between 48° 40´ 14´´ and 48° 41´ 55´´ longitude). It’s a crowded place which its population is increasing rapidly (Fig 1). This increasing rate of population growth is along with changing land use and making residential apartments instead villa houses. It censes some difficulties for water and wastewater networks due to seasonal heavy rains and river overflow. The gradient of zone is nearly 0.85-0.3 degree. Total length of water distribution network is 78.28 Km and it consist of 735 pipes and pipes are asbestos cement. This network is fed by a reservoir. Volume of reservoir is 50,000 m3 and it has a head which often is 110 cm. Water enters to network by 5 pumps which each of them are 3.25 m3/hour and their head is 6500m. Length of wastewater is also 57, 65 Km and consists of 4.3 concrete and asbestos cement pipes. Population of total zone is 60,000 and its extent is 5.3 Km.

2. Material and method

2.1. Data collection and quality control
The results of existing analysis in GIS are depended on the correctness of input data. Collected data are in digital form and consisted of geospatial and raw tables (Aronoff, 1989). These data included of water and wastewater network components (water pipes, pumps, valves, fittings, reservoir, waste pipes and manholes) and also pavements, parcels, river and rail. In the first phase, required descriptive data completed for network analysis. Then, repetitive rows and strange phrases removed, substituted and linke src=”https://www.geospatialworld.net/wp-content/uploads/images/application/d to features. Existing errors in geospatial data consisted of separation of components, overshoot, undershoot and multipart errors which are removed by definition of necessary tolerance automatically. Then, primary and foreign key attributes were defined for tables which were required in database for making relationships.

Fig 1: Location map of Kianpars and Kianabad portion of Ahvaz city SW Iran
2.2. Preparing conceptual model
For defining resources of real world from the viewpoint of data and application and also extension of systems for assembling, data model is avoidable. For designing conceptual model “Entity Relationship Diagram” (ERD) in this zone, 5 entities for water network (water pipe, fitting, pump, reservoir and valve) and 2 entities for wastewater network (waste pipe and manhole) are considered. This model which is designed for assembling geometric network is shown in fig 2 and 3. In ERD model, spatial relationship and scale of its cardinality are presented among entities. It also helps to establish connectivity rules among network components while establishing geometric network.

Fig 2: ERD of water supply network in Kianpars and Kianabad (Ahvaz city)

Fig 3: ERD of wastewater network in Kianpars and Kianabad (Ahvaz city)
2.3. Modeling hydraulic behaviors of water
For modeling hydraulic behavior of water in network pipes, it is used of Darcy-Weisbach formula (Gerlech et al., 1963) for calculating pressure loss. For calculation of existing drinking water demand in the network nodes, one period of customer’ water reading is used. By dividing scale of demand to length of period, base demand is calculate are liter/seconds can be gotten. There are 509 nodes in network which their base demand is 0.006 to 0.86 liter/seconds. Average scale of demand is 0.2147 liter/seconds which 45.78% of nodes are more than average.

For estimating the power of pumps in the network, the one–point pump curve used. Because pumps are parallel, by fixing head, discharges are added (Clark et al., 1971) and this formula is driven:

Head = 86.67 – 1.15 E – 6 (Flow)2

Following results are revealed:
Head loss in pipes: Amount of this parameter in 35.1% of total length of network is more than average which is located in centre and above of network where scale of demand and population is high. High demand of water causes high friction and consequently high head loss in this zone.

Head in nodes: Oscillation of water height in nodes is related to their remoteness of pump, zone topography and demand. In the studied zone, head changes in nodes are only related to remoteness of pumps because of low ground, and topography. Scale of discharge in pipes: There’s high discharge of water in transferring pipes, because only 25.98% of network length possessing discharge is more than mean. It is clear that first consumption is high in whole network and second there are points with high off take in network.

Pressure in nodes: Pressure calculated in nodes is based on zone gradient and customer base demand. Original reason of hydraulic changes including pressure in zone is the amount of population and increasing consumption counter. Apartment building and land use changing without regarding potential and the ability of pipes, can affect pressure changes in this zone. The least amount of pressure is 81.0179 m/pipe length and the max is 105.595. An average pressure in the zone is 87.886 m/pipe length, which 164 nodes (32.54% of customer) have high pressure. This amount of pressure has seen in nodes at network end which their demand is less than zone average. Also 69% of network suffers from pressure shortage in pipes in centre Kianpars where highest apartments exist. Isopressure curves are drawn in this zone for optimum management of pressure which is shown in Fig. 4.

By this the low pressure area can be determined and make good remedies to prevent features problem. By increasing population, principle problems due to shortage of water pressure are unavoidable like cuttings and finally rationing. Although, high pressure of water in pipes causes some difficulties like exploding. GIS helps network managers by analyzing and producing maps to control pressure and discharge in pipes. This can lead has to have a balanced network.

2.4. Geospatial database design
Establishment of relational database has sum deficient. Nature of geospatial data doesn’t appropriateness with in a list structures and SQL language is unable for geospatial concepts (Worboys et al., 1991). Even thought some researchers have offered ways for improvement of this kind of databases but (Seaborn, 1995) believe that object oriented database like geospatial database will abandon relational databases. On the base of this subject, for optimum management of water and wastewater network in Kianpars and Kianabad, a geospatial database is established called “Abfa”. According to organization needs, existing situations and future foresights, attribute domains are assigned to prevent entering attributes out of limitation (Booth et al., 2002).

Fig 4: Isopressure lines of water pipes in Kianpars and Kianabad (Ahvaz city)
This domain for water and wastewater pipes diameter called water pipe diameter and waste pipe diameter. Then three feature datasets are made according to available data including: wastewater dataset for wastewater network management, water dataset for water network management and city dataset for keeping and managing customer’ data and other civil features. Then feature classes are made in each dataset by importing digital data and tables which are in table 1. Then each feature dataset is applied a coordinate system for keeping connection of topology among data which its accuracy and dimension has shown in table 2. Subtypes are established for optimum supervision of networks based on the immediate needs and controlling behavior of feature (table 1). This is done for feature classes of pipe according to diameter, waste pipe according to gender, manholes according to internal diameter and fittings according to their type.

Relationship classes are established for modeling the connection of feature classes inside geospatial database (Zeiler, 1999). Wastewater-manhole relates (Wmrelate) relationship class is established among wastewater pipe feature and manholes inside wastewater dataset. Also pipe-valve relates (pvrelate) and pipe-fitting relate (pfrelate) relationship class is made among water pipe feature class and orderly valves and fittings inside water dataset.

Table1: Feature datasets and feature classes

Feature Class Name Type Geometry Subtype
City dataset
Address Feature Class Poly line
Boulevard Feature Class Poly line
Owner Feature Class Polygon
Parcel Feature Class Polygon
Pavement Feature Class Poly line – 
Rail Feature Class Poly line – 
River Feature Class Polygon – 
Wastewater dataset
Diameter pipe Feature Class Poly line
Manhole Feature Class Point 250mm,300mm, 500mm, 600mm, 800mm, Canal 
Waste pipe Feature Class Poly line Brick, Asbestos- Cement, Concrete  
Water dataset
Valve Feature Class Point
Depth fitting Feature Class Polygon – 
Fitting Feature Class Point Reducer- Increaser, Plug, Elbow, Tee
Pump Feature Class Point
Reservoir Feature Class Point
Station Feature Class            Point  
Water pipe Feature Class            Poly line 100mm, 1200mm, 1400mm, 150mm,  200mm, 250mm, 300mm, 500mm, 600mm, 80mm

Table2: Applied coordinate system, dimension and precision of it

Dimension Minimum Maximum Precision
City, Wastewater, Water
X 271112.1123 288291.9815 124999.9999
Y 3462524.186 3479704.056 124999.9999
Z 0 21474.83645 100000
M 0 21474.83645 100000
Coordinate System: WGS_1984_UTM_Zone_39N

By relationship classes we can remove most of needs of assistant group without analyzing of geometric network. For example; finding the number of manholes and their attributes which are located on a damaged pipe, accessing to pipe information which closed manhole is located on it and similar examples in water division network. Relationship classes are defined as composite type because components of network are completely related to each other. In these classes, water and waste pipe are considered as origin class and fitting, valves and manholes are defined as destination class. These relations are established because of affiliation of a destination class to origin with gender of cardinality 1-M. Messages direction of both and path label is chosen according to message direction because the connection among classes becomes enforceable in both states, pipes to components and vise versa. For example; when fitting has a problem, we can access to pipes in their tables of water network by choosing and enforcing relationship class with water pipe path label. Properties of Relationship class features are in table 3.

Table 3: Properties of Relationship class features in datasets

Name Origin Destination Composite Rules
Anno_depth fitting Depth fitting No No
Anno_diameter wastepipe diameter_pipe No Yes
Anno_parcel parcel owner No No
pfrelate waterpipe fitting Yes Yes
pvrelate waterpipe valve Yes Yes
wmrelate wastepipe manhole Yes Yes

Established subtypes indicate details of water and wastewater network, but sometimes there is information that user needs immediately and searching in database in base wastes time. For presenting this information without searching in database, it can be used of annotation classes. So for presenting customers and parcel owners, an annotation class is formed in city dataset called owner. Also for present depth of fittings when appearing accident or replacement depth fitting annotation class is formed in water dataset to represent diameter of pipe well. Annotation class called diameter of pipe is make in wastewater dataset, too that waste pipe’ diameter can be present well.

Designed geospatial database for different organizations change continually for keeping data up to date and civil zones change continually due to population grows in studied zone. This subject causes to edit existing features and import new features by changing land uses. For preventing errors into geospatial database while editing or digit new features, topology rules are formed. It should be considered that geometric network is a complete topological model witch connectivity rules apply in its. Topology rules are defined for the features that do not participate in geometric network. These rules are introduced as city-topology in city dataset. Because of participating features in this rules are equal clearness level and accurate as snapping features; previous features most not have much movement. For equaling the scale of feature movements when applying rules, it is taken similar ranks (=1) to all features. Characteristic of this rules are in table 4.

2.5. Geometric network design
For modeling water and wastewater network, water-net and wastewater-net is established in water and wastewater datasets.

Table 4: characteristic of topology rules

Name: city_topology Cluster Tolerance:0.0000164841
Maximum Generated Error Count:Undefined
State: Unanalyzed
Feature Class  Weight XY Rank Z Rank Event Notification
Address  5 1 1 No
Boulevard  5 1 1 No
Parcel  5 1 1 No
Pavement  5 1 1 No
Rail  5 1 1 No
River  5 1 1 No
Topology Rules
The rule is a line-no overlap other line rule 
The rule is an area-no overlap rule 
The rule is a line-no self overlap rule 
The rule is an area that does not overlap another area 

In both network, pipes are modeled as complex edge. This subject is so important because if they modeled as simple edge where there’s a sub pipe or customer branch is connected to each pipe, an orphan-junction is established and geometric network accepts its as a feature which can act as some features that connection with some elements in logical network. Manholes, fittings, valves and reservoirs are modeled as simple junction while pump station is modeled as complex junction. Then it is applied to manholes and reservoirs the ability of source or sink (ancillary role).

Speed and direction of wastewater in pipes is a function of pipe and manhole’s diameter. So it is defined weight to wastewater geometric network according to these characteristics. Weight of edges is related to field of diameter and weight of junctions is related to field of Inner diameter of manhole (Dia-inner) in tables of these feature classes. Then connectivity rules and cardinality are established according to network pattern. All defined rules are edge-junction type.

Water pipes are weighted according to pipe’s diameter and discharge in its which have been modeled previously. Also pressure weight to nodes, head weight to reservoir and power weight to pumps are defined. Existing connectivity rules in this network include edge-junction and edge-edge that pipes must joint during a fitting.

2.6. Management systems and analysis of geometric network

2.6.1. Modeling flow direct
In wastewater geometric network due to absence of pressure source wastewater follow ground gradient. For modeling flow direction, lowest manholes of zone are considered as sink (or exit point). In water geometric network, reservoir is considered as source.

2.6.2. Tracing equipment inside the network
For any reason like an incident or replacement of components inside wastewater, first step of assistant group after informing of event is to cut the water in pipes of the zone. In addition, fast action and cutting the water in pipes it prevents wasting water. Designed geometric networks have useful analysis for accelerating to investing the events inside the network.

When a wastewater pipe obstructs or an accident happens like breaking, finding pipes in upstream of network are helpful. This work, in mechanized networks helps emergency group to cut the movement of wastewater from the zone to remove the event and transfer to other pipes in some cases, the reason of event in one place is pipe stuck or manhole in down stream (fig 5) of zone. Finding upstream and downstream path is enforceable in geometric network by existing analysis. In water-net, valves are recognizable which must close that part of network like; a damaged pipe becomes dry (Fig 6). Also common valves can be found when two points are damaged. Also a valve is needed which cut the water of damaged place and don’t cut the water of important customer like hospital, school and etc. in looped systems, unsuitable fittings in network cause that some valves are recognized which must close orderly to present their effect in network. Also by these analyses can recognize pipes with less of water by closing one valve or find the scale of low of pressure.

2.6.3. Control building and digging operation
Defaults of wastewater-net and water-net can control building and digging operation in the zone, especially streets which dig by persons or different organizations with existing descriptive information about depth of equipments inside database which update by its manager continuously. For acceleration of this subject, establishing an annotation class can be so useful because it presents the depth of equipments without searching inside database like; depth fitting annotation class.

3. Results and discussion
Changes in Pressure and other hydraulic behaviors of water pipes in the zone are related to demand and scale of nodes. So topography and gradient have least effect.

Classification and analyses of pressure in the zone show that demand and discharge of water in pipes are high in centre of zones that located in Kianpars especially around main streets like; Shahid Chamran, Eidoon and Khordad. So in these parts of network pressure is low.

There are parts witch have high demand and vice versa which is due to unequal population growth and consequently unbalanced pressure in the zone.

Relationship class can be more effective than network analysis in most cases like; finding fittings of a pipe, because it joints feature class tables together. Relationship class must define with kind of composite, because of relation between network components. Annotation class can give user much information of network features without searching immediately in water and wastewater network. For modeling flow direct in wastewater network, any source doesn’t apply to intersections due to lack of productive source of energy in this net. Because of following wastewater movement of zone gradient, lowest manholes consider as sink. In water network, reservoir considers as a source and causes flow. Analyzing the network can find upstream and downstream path of one part of network like; damaged pipe. Also it can be found valves which cut the water in pipes of that part which becomes less of water by closing one valve or the common valve.

Fig 5: Downstream analysis in wastewater network in Kianpars and Kianabad (Ahvaz city)

Fig 6: Upstream analysis in water network in Kianpars and Kianabad (Ahvaz city)
  • Aronoff, S. (1989). Geographic Information System: A Management Perspective. WDL Publication, 1st Edition, Ottawa, 313p.
  • Bahadur, R., Pickus, J., Amstutz, D., Samuels, W. (2000). A GIS-based water distribution model for Salt Lake City. Science Applications International Corporation, 1410 Springhill Rd., McLean, VA 22102.
  • Bonniface, J., Coppins, G.J. (2007). Particle data management—turning data into accessible information. Environmental Projects Department, UKAEA Dounreay, Thurso KW14 7TZ, UK, Volume 18, Issue 3, p. 230-254.
  • Booth, B., Crosier, S., Clark, J., MacDonald, A. (2002). Building a Geodatabase GIS by, ESRI. 380 New York Street. Redlands, CA 92373-8100, USA, 468p.
  • Clark, J.W., Viessman, W., Hammer, M.J. (1971). Water supply and pollution control. 2. Edition, International Textbook co., London.
  • De schaetzen, W., Boulos, P.F. (2001). Optimal water distribution system management using ESRI MapObject technology. North Lake Avenue, Suite 1200, Pasadena, CA 91101, .
  • Gerlech, E., Brix, E.J., Heyd, H., Hunerberg, K. (1963). Die Wasserversorgung 6. Auflage, Oldenburg, Wien, Munchen.
  • Mathiyalagan, V., Grunwald, S., Reddy, K.R. (2004). A WebGIS and geodatabase for Florida’s wetlands. Computer and Electrical Engineering, University of Florida, Volume 47, Issue 1, P: 69-75.
  • Nielsen S.J. (2005). The City of Ballerup, Denmark, Integrates Urban Water Modeling and GIS. Ballerup Kommune, tel.: 45-44-772-339, Arc News, .
  • Nobel, C.E. (1998). A model for industrial water reuse. A geographic information system (GIS) approach to industrial ecology, MSc thesis, University of Texas at Austin, Austin, p 142.
  • Ramirez, A. (1997). Interfacing Potable water hydraulic models with ArcInfo and ArcView data set: From start to finish. Anchorage Water and Wastewater Utility, 3000 Arctic Blvd, Anchorage, AK 99503.
  • Rangzan, K., Mehrabi, A. (2007). Establishing a geospatial database and geometric network system for management of water distribution network of Kianpars and Kianabad urban district, 1st Urban GIS conference , Shomal University, Amol, Iran, 13p (In Persian).
  • Seaborn, D (1995). Database management in GIS-past, present, future: an enterprise perspective for executive. Seaborn Associates, 475 Cloverdale Road, Ottawa, Canada.
  • Worboys, M.F., Hearnshaw, H.M, Maguire, D.J (1991). Object-oriented data modeling for spatial database. International journal of geographical information system, 4 (4): 369-83.
  • Zeiler, M. (1999). Modeling our World: The ESRI Guide to Geodatabase Design. Published by Environmental Systems Research Institute, Inc., 380 New York Street, Redlands, California 92373-8100, 202p.