INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH, VOL 1, ISSUE 5 6 ISSN

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1 INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH, VOL 1, ISSUE 5 6 Field Case Study To Validate A Geospatial Approach For Locating Leakage In Water Distribution Networks - Field Data Collection And Modeling Analysis. (Case Study In Shah Alam, Malaysia) Salah Muamer Aburawe, Ahmad Rodzi Mahmud, Thamer Ahmad Mohammad, Noordin Ahmed Civil Engineering Department, Faculty of Engineering, Universiti Putra Malaysia Serdang, Selangor, Malaysia. salah_aburawi@yahoo.com ABSTRACT: It obvious to all people the importance of water as an essential element for the life, so the water loss is a life-threatening and alarming predictor of the future. Leakage problem is one of the most important causes of water loss in water systems, therefore it was and still a matter of attention of many researchers in search of the most effective methods to solve this problem using many techniques varied with one another in terms of accuracy, cost and speed of obtaining results. This research paper refers to ongoing extensive research work to develop a new geospatial approach to detect leaks in water distribution networks, and reviews a summary for field data collection procedures and modeling analysis within the field case study that has been done to validate the approach referred to. Keywords: Leakage detection; Water distribution networks; GIS; Hydraulic modeling 1 INTRODUCTION 1.1 Background Water means money, even it is more valuable than that, it means life itself. So, before thinking in the search for new water resources, should be think to preserve what it is available right now. The leakage in water supply networks is the most important threats facing the water resources of any country, especially in countries that already have a lack in their water resources. To address this problem must search for effective techniques to detect and locate the leakage in real-time to prevent exacerbate of the problem. Just find a method to detect the leakage is no longer the main problem now, there are many methods and techniques currently used to perform this task right now, but the question that arises now is How effective of these methods to solve such problem radically and effectively?. The traditional methods of leakage detection in water distribution networks, which rely to acoustic techniques are time consumed and costly. In addition to the weakness of their efficiency in case of using non metallic pipes, which made many researchers around the world tend to develop analytical methods based on the high analytical capabilities of the computer to resolve this problem. Based on above can concludes that can evaluates of any method depending on three key factors, which are Accuracy, Cost and Time, where these factors can give indications about the efficiency of that method and its effectiveness to do the task. Therefore, the problem of leakage detection not just lies in finding a method to do that task, It must be an ideal method characterized by high accuracy, save cost and Provision results in real-time. In this regard, there is an extensive study currently being conducted to develop a geospatial approach to detect and locate leaks in water distribution networks based on analytical methods based on field measurments can be obtain in real-time using SCADA system. To complement verification the effectiveness of that approach practically, a field case study had been conducted using an existing water network. The work involve the verification of the ability of the approach to detect leakage in the field, and study the impact of network size and operating conditions on the effectiveness of the approach.the researcher conducted this task in collaboration with JALUR CAHAYA company, which is one of the specialized Malaysian companies in the field of water loss. Several discussions were held with JC company concerning the field work project. Field work plan has been designed and agreed by JC in this regard In this research paper, the author refers to the extensive research work and provides an describtion for field data collection procedures and modeling analysis which have been done to assess and calibrate the hydraulic model for use later to validate the developed approach. It should be noted here that the field study conducted in a selected DMA in Shah Alam city, Selangor, Malaysia. 1.2 Study Description This field case study is a part of an extensive research work aims to design and develop an approach for real-time leakage detection and location in water distribution networks using a geospatial technology as a base system with acceptable accuracy results and reasonable cost (Figure 1.). Objectives to reach the research aim are: Develop an analytical approach that can be used to detect leakage in water distribution networks in real time with acceptable accuracy results and reasonable cost. Propose a geospatial integrated system for applying the analytical approach to perform the task of leakage detection and location. Conduct lab experiments and Field works to validate the developed approach.

2 INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH, VOL 1, ISSUE Leakage Modeling Modeling leakage in water supply networks is important due to some problems represented in quality of service, cost of system expansion and wasting energy resources. Monitoring the pressure in certain parts of network and then finding a relation between pressure changes and leakage rate is a quick way for detecting leakage. This relation is nonlinear and complex for modeling. [4] 3 METHODOLOGY Figure 1. Geospatial Approach for Real-time Leakage Location The scope of this research paper is fucuse on a part of the tird objective, which is field works and limited to the field data collection procedures and modeling analysis. 2 LITERATURE REVIEW Computer models for analyzing and designing water distribution systems have been available since the mid-1960s. Since then, many advances have been made regarding to the development and application of this technology. Was the main catalyst for the growth and use of computer models is the rapid development and widespread use of the computer and its applications. With the implementation of this technology, water utilities and engineers have been able to analyze the existing system as well as to investigate the impacts of proposed changes. However, the validity of these models depends largely on the accuracy of the input data. Before an actual water distribution system can be modeled or simulated with a computer program, the physical system must be represented in a form that can be analyzed by a computer. This normally requires to represent the water distribution system by using node-link characterization. In this case, the links represent individual pipe sections and the nodes represent points in the system where two or more pipes join together [1]. 2.1 Data Modeling and Model Parameters Typically, model data of water distribution network is associated with two entities, which are the links and the nodes, where links represent pipes, pumps, valves, while nodes represent junctions, endpoints, water sources and demand points. The model also includes the characteristics of facilities such as pipe include length, diameter, roughness or friction coefficient of the pipe, as well as related operational parameters, in addition the model database containing metadata and descriptive information needed to determine, organize and manage the model. [2, 3]. Once the data for the computer network model has been assembled, the associated model parameters should then be determined before actual application of the model. In general, the primary parameters associated with a hydraulic network model include pipe roughness and nodal demands. Because obtaining economic and reliable measurements of both parameters is difficult due to the uncertainty of those measurements in most cases, so there is a need to calibrate the model to get more reliable results. Model calibration involves the adjustment of the primary network model parameters (i.e., pipe roughness coefficients and nodal demands) until the model results closely approximate actual observed conditions, as measured from field data. [1] 3.1 Methodology Flowchart Figure 2. illustrates the procedural steps that have been undertaken to complete the field work, which began by selecting the study area and to prepare a work plan to conduct all practical tasks in the field. Based on the work plan, data loggers have been installed at certain monitoring nodes for collecting flow and pressure field data in specified period of time (one week) in order to determine the hydraulic behavior of the network in the absence of a leak, this filed data will be used later in the process of hydraulic model calibration. Figure 2. Methodology Flowchart After a week, other scenarios have been conducted for fabricating leaks in the network by opening a certain fire hydrants at a specified times with known flow rates for limited periods of time. Flow and pressure field data were collected in each case during and after the leakage for comparing with modeling results later in the process of locating the leakage.based on base data available about the network (network layout, pipe materials and diameters, node levels, operational data.. etc.) as well as the node demand data, which can be obtained from billing data for the consumption points during period of time, it can be built and calibrated the hydraulic model using the field data that has been collected in the first phase of field work.finally, the calibrated model was used to apply the approach of leak detection and verify the extent of match between leakage locations that have been obtained by the approach and the actual leakage locations that were fabricated in the field.it is worth mentioning here that the scope of this research paper is limited to first, second and third phases. Forth phase and final results of the extensive research will be explained in detail in

3 INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH, VOL 1, ISSUE 5 8 another research paper later. 3.2 Study Area Figure 3. shows a satellite image for the DMA which has been selected as a study area. The study area is located within the administrative area of Shah Alam district in Selangor state. The DMA covers an area of 175,000 square meters and divided into two zones (residential and commercial). The DMA is supplied with water from the main network of the city, and the pressure inside DMA is controlled by using PRV which has been installed at the entrance to the DMA. collect field data for the identification of flow patterns, which one of the requirements to build the hydraulic model. The second phase was aimed to fabricate a series of leaks at specific locations (fire hydrants) with a specific volume of leakage to determine the resulting changes in flow and pressure at the network nodes. Installation of data loggers As shown in Figure 4., seven data loggers were installed in different locations in the DMA to monitor flow and pressure readings within a period of one week every 15 min without causing any external influences on hydraulic behaviour of the network, Figures 5., 6. And 7. illustrate images for installation of some of data loggers. Figure 05. Measuring pressure before data logger installation Figure 3. Study area (Source : Google Earth) 3.4 Field Data Collection Field data were collected using data loggers which have been installed at fire hydrants in specific locations to monitor and record pressure readings every 15 minutes over ten days. Beside that, there was another data logger at the entrance of the DMA to collect flow data. Figure 4. shows the pressure monitoring locations, which are presented in the model at the following nodes (J-136, J-156, J-147, J-152, J-142, J-154 and J- 140). Figure 6. Data loggers installation Figure 7. Data loggers after installation Figure 4. Pressure Monitoring nodes (Data Logger Locations) Field data were collected in phases, first phase was aimed to collect pressure and flow readings within a week period to identify the hydraulic behaviour of the network, as well as to Fabricating Leakage After a week, on the eighth day, leaks have been fabricated in certain locations at fire hydrants for the purpose of studying the impact of these leaks on the pressure at the rest of network nodes, this step begun by measuring the flow that can be obtained from the fire hydrant, which ranged between L/min, then later collecting pressure readings from data loggers which have been installed before one week.leaks fabricating process have been conducted in day-time and nighttime periods in order to include all the demand times within 24 hours. Figure 8. shows samples of this step.

4 INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH, VOL 1, ISSUE 5 9 Figure 10. shows the network map including all base data needed to build the hydraulic model.. Figure 08. Leakage fabricating at day-time and night-time Table 1 shows information about nine leaks that have been fabricated in eight locations : TABLE 1 INFORMATION ABOUT FABRICATED LEAKS Time Time Leak size Leak no. Node Date start end (L/s) L01 J /12/ :18 24: L02 J-97 13/12/ :13 01: L03 J /12/ :53 02: L04 J-12 13/12/ :32 11: L05 J /12/ :11 11: L06 J /12/ :51 12: L07 J /12/ :28 13: L08 J /12/ :05 17: L09 J-88 13/12/ :48 18: Figure 9. shows the locations of the fabricated leaks in the model map, where it was used one of them twice, one during night-time and the second during day-time, which is at node (J-141), while the rest of the locations were used once during day-time. Figure 9. Locations of Fabricated Leakage 3.4 Network Modeling Base Data collection and conducting required correction Collaboration with JC company base data was obtained to build a hydraulic model for DMA network, base data was as follows: Base map shape files including Buildings, Lots and Roads. Water network shape files including pipelines, hydrants, valves, meter and PRV. Figure 10. Base data for DMA network After reviewing base data was found that it contains some errors which may cause problems during the building of the hydraulic model making it unsuitable for model building in its current situation. Some modifications and correction processes have been conducted to improve base data which were as follows: Reconnection of unconnected pipelines at nodes points. Rearranging the fields of attribute data commensurate with the hydraulic model building. Redistribution of the network nodes, and create new nodes as needed. Hydraulic Model Building Model builder tool in WaterGEMS has been used to import model data from network shape files and build the hydraulic model by following steps: a) Running ModelBuilder tool from tools option. b) Creating a new model file by choosing new button. c) Selecting data source type as Esri shape files and browsing for the source shape files. d) Adjusting the connectivity options by specifying the coordinate unit and tolerance distance. e) Adjusting the options of element / remove / update options. f) Leaving the additional options as they are appear in the system. g) Specifying field mapping for each table in the source shape files. h) finalizing the procedures and creating the model. Estimating Node Demands Node demands have been calculated by using Billing data which have been obtained from recordings of water meters at the point of consumption for specific periods of time. Node demand estimating has been done using Loadbuilder tool in WaterGEMS by following steps: a) Running LoadBuilder tool from tools option. b) Creating a new file by choosing new button. c) Adjusting the demand data source as Point load data and selecting the method of load distribution as Nearest Node.

5 INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH, VOL 1, ISSUE 5 10 d) Browsing for node layer and billing meter layer and specifying the load type field as Pattern and usage field as Demand by L/s. e) Assigning the pattern for each load type. f) Confirming the list of calculation loads for each node. g) Finalizing the procedures by exporting the loads of nodes to a new alternative. Estimating Node Elevation Node elevations were estimated using grid point data of the study area, The grid point file contains elevations and GPS data for points. TRex tool in WaterGEMS has been used to estimate the node elevations through the elevation data of the closest grid points. The main steps to perform this task as following: a) Running TRex tool from tools option. b) Selecting data source type, browsing for the elevation dataset file and selecting the elevation field. c) Completing the TRex wizard by exporting the node elevations to a new alternative. 3.5 Data Analysis and Evaluation Scenarios and Alternatives For analyze the field data, set of scenarios and alternatives has been proposed for perform a range of hydraulic analysis, and draw conclusions for the purpose of identifying the hydraulic behavior within the network theoretically, and conduct comparisons with field data measurements and modeling results as an initial step to perform model calibration. The proposed scenarios for this analysis were as follows: 1. Normal case scenario (without leakage): In this scenario, no external influence was made on the usual hydraulic behavior of the network. This is to determine the changes of flow in the main pipe to the network (P-95) which supply water for the whole network, and also the pressure changes at pressure monitoring locations (see Figure 4.). 2. Leakage scenarios: These are the most important scenarios, where several leaks were applied as a demand in the hydraulic model to simulate each of fabricated leaks in the field. This to determine the flow and pressure changes occurring as a result of fabricated leaks. These scenarios must be done with an accurate calibrated model. e) Selecting data source type as an Excel file and browsing for the data file Which was prepared in advance and contains field data required for calibration process. f) Adjusting spatial and connectivity options as required. g) Leaving the element create / remove / update and additional options as they are. h) Specifying field mapping for each table. i) Exporting all field data needed for calibration to Darwin Calibrator tool. j) Rerunning Darwin Calibrator once again, all field data will be appears in the calibration study which has been created. k) Creating the Roughness Groups for the calibration study and selecting the pipes for each group. l) There are three roughness groups which are AC, MS and UPVC depending on the pipe material. m) By using the same steps, the demand groups can be created. Two demand groups were created depending on the patterns of demand distribution either it for Commercial (Com) or for Residential (Res) use. n) Creating New Optimized Run within the calibration study. o) Adjusting the roughness and demand groups and field data. p) Running the calibration study. q) conducting several adjustments on roughness and demand groups until reach the minimum fitness. r) Exporting the results of the calibration to a new alternative and scenario. 4 RESULTS AND DISSCUSION Field study was done to identify the hydraulic behaviour of the network and identify patterns of flow and pressure for each of the working days and holidays, in addition, field data have been used for model calibration processes. 4.1 Field Data Analysis Flow Field Data Analysis Graph in Figure 11. shows the records of flow field data in the main pipe (P-95) at the entrance of the DMA within the period of field study, which were recorded for every 15 minutes. The graph shows how the flow changed during each day of the field study period. With reference to the hydraulic modeling results, the hydraulic model need to calibrate, although the field data and initial hydraulic model were good enough to clarify the hydraulic behavior of the network in general, and give a good idea about the changes in the flow pattern in the commercial and residual zones. However, this model without calibration cannot be used in the leakage detection processes, so it must perform a calibration of the model using field data available. Model Calibration Calibration process was performed using Darwin Calibrater tool which available in WaterGEMS by following steps: a) Running Darwin Calibrator tool from Analysis option. b) Creating new study named Leakage Detection Study. c) Using ModelBuilder tool from tools option to upload field data for calibration. d) Creating new ModelBuilder file. Figure 11. Flow Readings within Field Study Period

6 INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH, VOL 1, ISSUE 5 11 To determine an accurate average flow pattern during the week, days of the week were divided into working days and holidays to reduce the level of noise in the results resulting from the apparent change in the pattern of demand during week days, also for getting a hydraulic reference simulates the reality more accurately. The Figures 12. and 13. illustrate the pattern of average flow in the network during both of working days and holidays. Figure 12. Average Flow Pattern during Working Days In both cases it can be considered the average flow graph (green graph) is the hydraulic reference that can be used for comparison to make sure that the flow in the network free of any flows other than the demands. To detect such flows like leaks, their volume should be outside of noise area (The area between blue and red graphs), i.e. the leak volume should be higher than the value of the maximum flow (blue graph), otherwise the leak will be considered as a noise, and will not be detected. Figure 13. Average Flow Pattern during Holidays Pressure Field Data Analysis Graphs in Figure 14. show the area of changing in pressure and the average values of the pressure at each of observation nodes (J-136, J-156, J-147, J-142, J-154 and J-140) during weekdays, which can be considered as Pressure Footprint (PFP) for those observation nodes (blue graph), where the pressure distribution in this pattern can not be repeated with the same values and shape in any other node, so it is serve as an pressure footprint for that certain node. Figure 14. Pressure Footprints at observation nodes (Weekdays) Model Calibration Results After performing the hydraulic analysis, It is noted that there are a differences in the pressure values between field readings and hydraulic modeling results, especially in the period from 6:00 am to midnight, as a result of an errors in the estimation of node demand or pipe roughness during model building phase, which is an expected, so it is necessary to conduct calibration process for the model to be relied upon in leakage detection phase. Darwin calibrator tool in WaterGEMS has been used for calibrating the model. Graphs in Figure 15. And 16. show the comparisons among field readings and modeling results before and after calibration process at the monitoring nodes. Figure 15. Comparison among flow field readings and modeling results for both non-calibrated and calibrated model

7 INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCEMENTS AND EMERGING ENGINEERING RESEARCH, VOL 1, ISSUE 5 12 Figure 16. Comparison among pressure field readings and modeling results for both non-calibrated and calibrated model 5 CONCLUSION AND FUTURE WORK This research paper reviewed a part of an extensive research work to develop an approach for detecting and locating leaks in water distribution networks based on the principle of leakage simulation through hydraulic analysis scenarios.scope of this research paper was limited to clarifying the phase of field work, building of the hydraulic model of the network and performing the model calibration process. These steps were the basis to provide validation requirements for the proposed approach.despite the challenges faced the field work which can be represented in the lack of accuracy and sufficient credibility in base data of the network with regard to the pipes roughness and demands at the point of consumption, but the results of the field work showed great consensus with what is expected and targeted, especially after performing a calibration process for the hydraulic model. As a future step, the developed approach will be applied to detect leaks through simulation processes for the leaks that were fabricated in the field using the calibrated model, and make sure that leakage locations can be detected properly. This subject will be covered in the subsequent research paper, which will explains the results of the application of the approach and its ability to locate leaks accurately. ACKNOWLEDGMENT The authors wish to thank all those who contributed the success of this research work, in particular the team work of JC company that had provided all supports to complete the fieldwork phase of this research. REFERENCES [1]. Ormsbee, L. and Lingireddy, S. Calibration of Hydraulic Network Models. The McGraw-Hill Companies, Inc., City, [2]. Trammel, D. Creating a Hydraulic Model from a ArcSDE Geodatabase. City, [3]. Grise, S., Idolyantes, E., Brinton, E., Booth, B. and Zeiler, M. ArcGIS Water Utilities Data Model. Esri, City, [4]. Jalalkamali, A. and Eftekhari, M. Estimating Water Losses in Water Distribution Networks Using a Hybrid of GA and Neuro-Fuzzy Models. World Applied Sciences Journal2012).