Water losses in water distribution systems reach significant rates depending on the network characteristics. Various methods, which have initial investment and operating costs, have been applied to reduce these losses. Therefore, appropriate and applicable methods should be preferred by considering the network characteristics. The aim of this study is to determine the economic loss level with an optimization algorithm for utilities with different network characteristics, water production, operating costs and institutional capacity. Three pilot utilities with different system characteristics and water loss components were selected as application areas. The non-revenue water rates are currently calculated as 57%, 50% and 37%, respectively. The economic loss levels in the pilot utilities were calculated as 29%, 16% and 23% with the optimization model. Moreover, the most appropriate methods to be applied according to the conditions of the utilities were determined in order to reach these defined economic loss levels. It is thought that the results obtained from this study will be a reference for the development of sustainable water loss management strategies and their implementation in the field.

  • The economic loss level was analyzed with an optimization algorithm.

  • The economically recoverable loss volume was determined.

  • The economic loss levels were defined with different networks/system characteristics.

  • The developed model was tested with field data.

  • The most suitable water loss prevention methods were determined.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Water losses, which consist of apparent losses and real losses, are one of the most fundamental problems faced by utilities (Farley & Trow 2003; Farley & Liemberger 2005). The service quality decreases, operating costs increase and network operating conditions deteriorate depending on the increase in water loss rate. Especially complex and old water distribution systems (WDSs) generally have high failure rates. Moreover, it is quite difficult and costly to detect, prevent and control unreported leaks in these systems (Lambert 2002; Farley & Liemberger 2005). The following works should be made for sustainable water loss management (WLM) in WDSs; recognizing and preventing the water losses, monitoring and controlling the system with information systems, analyzing and monitoring the system performance and defining the economic analysis standard (Firat et al. 2021; Bozkurt et al. 2022).

Globally, water loss rates in developing countries range from 24% to 45% (Kingdom et al. 2006). Water loss rate reports are published annually by the General Directorate of Water Management (SYGM) in Türkiye. Water loss rates in utilities (a total of 30 utilities) were around 34% in 2021 (SYGM 2022). In the reports prepared by the Turkish Water Institute (SUEN), the economic losses caused by water losses for utilities are approximately 7 billion TL (Turkish liras) (SUEN 2022). As can be seen, water losses do not only cause water resource inefficiency. They also create the costs of energy, apparent losses and real water losses. Therefore, reducing water losses is quite the important and critical issue.

Targets for water loss rates in all utilities are defined as 30% (in 2019) and 25% (in 2023) in the ‘Water loss prevention and control regulation’ published in Türkiye in 2014. However, since these defined targets could not be reached, the regulation was updated in 2019 and the targets were redefined as 30% in 2023 and 25% in 2028. Accordingly, it is understood that it is not realistic to set the same target for each utility. For this reason, the current conditions of the system, technical and personnel capacities should be considered in order to reduce the non-revenue water (NRW) to an acceptable level (Yilmaz et al. 2021).

The methods and processes used to manage the NRW involve direct economic costs. The main leakage detection and reduction methods (pressure management (PM), pipe material management (PMM), active leakage control (ALC) and failure management) require knowledge of the current network conditions and sufficient technological capacity (Lambert 2002; Frauendorfer & Liemberger 2010; Sechi & Zucca 2017). The basic equipment and devices (pressure reduction valves (PRV)) for PM and acoustic listening devices and regional noise loggers or correlators for ALC (Salguero et al. 2019), have been used for detection and reduction of water losses. However, the following question arises: ‘will the expenditures to reduce water loss be financially returned to the utility? (Firat et al. 2021; Yilmaz et al. 2022). Therefore, utilities should define the ideal water loss rate according to the current system status. The main issue in WLM is the definition of the economic leakage level (ELL) (Farley & Trow 2003; Pearson & Trow 2005; Islam & Babel 2013; Haider et al. 2019; Firat et al. 2021; Yilmaz et al. 2021, 2022).

The ELL can be defined as the NRW value at which the economically recoverable loss in a WDS equals the water production/sales cost. Reducing water losses in WDSs below the calculated ELL value will no longer be economical for utilities because the amount to be spent for loss reduction is more than the marginal benefit of the water to be obtained. Considering the current state of the network, economic conditions, costs and institution personnel, technical and financial capacity for the most appropriate loss level in WDSs will provide a more accurate approach (Deidda et al. 2014). The ideal loss level in systems where water production and network operating and energy costs are high will be lower than for gravity systems. Therefore, it is necessary to define the most appropriate water loss rate, i.e. the ELL value, according to the current network conditions (Wyatt & Alshafey 2012; Lim et al. 2015; Molinos-Senante et al. 2016; Haider et al. 2019). Various approaches based on mathematical and statistical methods are applied in ELL analysis (Choi et al.2015; Haider et al. 2019; Ahopelto & Vahala 2020; Moon 2020; Moslehi et al. 2020).

In the management of leakage in WDSs, the definition of the ELL is a real-time optimization problem based on the network characteristics (number of reported or unreported failures, number of leak detection and management teams, pressure limits and current situation), the requirements and constraints of prevention methods and their cost analysis (Firat et al. 2021). Utilities have applied methods that create high costs in order to reduce leaks, which is the most important problem in WDSs. A short-term ELL analysis model was developed for the ALC method, considering the cost components and field data. Model results showed that system operating pressure and infrastructure conditions directly affect the short-run ELL value (Moslehi et al. 2021).

Analyzing the operation pressure that can cause new failures or increase leakage volumes, defining the ideal operation pressure according to the network characteristic and defining the number of failure repair teams for failure management are the optimization variables. A model applying an optimization algorithm has been proposed in order to analyze the ELL, considering all the factors that cause failure and leakage, analyzing the effects, defining the costs and performing the cost–benefit analyses (Yilmaz et al. 2021). However, the budget allocated for the management of water losses in utilities is limited to a certain percentage of revenues. In other words, budget constraints should be considered for WLM practices (Yilmaz et al. 2022).

Considering the existing studies in the literature, network characteristics, water production and operating costs and the financial–technological–technological capacity of the utility should be considered in order to create a sustainable and long-term WLM plan. It is quite important that the ELL value should be analyzed for each system based on these parameters. For this reason, the aim of this study is to determine the ELL value with the optimization algorithm for the utilities with different network characteristics, water production and operating costs and institutional capacities. To achieve this aim, three pilot utilities with different system characteristics and water loss components were selected as application areas. The ELL values were analyzed and compared by considering real field data. Moreover, the leakage reduction methods (PM, PMM, ALC and failure management) that should be applied in these networks with different characteristics, depending on the calculated ELL values, were determined and the possible benefits for each method were calculated. Thus, in this study, the most appropriate water loss target (ELL) was defined depending on the characteristics of the network, the methods to be applied to achieve this target were defined and the potential benefits to be obtained from these methods (the amount of leakage saved to the system) were calculated.

Failures in WDSs occur due to various factors that cause operating conditions to deteriorate, increase costs, increase customer complaints and decrease service quality. In sustainable urban water management, it is quite important to reduce the failure rate and leakage amount, to ensure water and energy efficiency and to reduce investment and operating costs. Therefore, the most appropriate method should be applied to reduce the failure and leakage rates. The main methods applied in WLM are the district metered areas (DMAs) method (Alvisi et al. 2019; Creaco & Haidar 2019; Liu & Lansey 2020), minimum night flow (MNF) analysis (Hazelton 2019; Negharchi & Shafaghat 2020), leak localization and repair (Ociepa et al. 2019; Sophocleous et al. 2019) and material management (Salehi et al. 2017; Fridman-Bishop et al. 2019). The DMA approach is a method applied to manage water losses in distribution systems in a more effective and sustainable way. The main purpose of the DMA method is to obtain a controllable and manageable system that is isolated from other limited networks. In this method, system components are evaluated within themselves (Pearson 2019). The MNF analysis is a method applied to detect leaks in DMAs. In this method, the zone inlet flow is measured with a flow meter and potential preventable leaks are evaluated according to the inlet flow rates at the hour of the lowest consumption.

Although it is possible to monitor, detect, control and manage leaks by applying these methods, the requirements, limitations, investment and operation costs of the methods reach significant levels. Therefore, considering the operational components of the system, performing economic analyses for water loss components and defining the benefit and cost analysis model is quite important for effective and sustainable WLM (Mutikanga et al. 2013; Ezbakhe & Pérez-Foguet 2019; Jensen & Nair 2019; López et al. 2019). However, inefficient and costly investments are required to achieve the water loss rate targets defined for the utilities in WLM. For this reason, it is recommended that analysts determine the current conditions of the system and define the ELL by performing economic analyses in choosing the most appropriate prevention strategy (Lim et al. 2015; Molinos-Senante et al. 2016; Haider et al. 2019). Various approaches based on mathematical and statistical methods have been applied for ELL analysis (Choi et al. 2015; Haider et al. 2019; Ahopelto & Vahala 2020; Moon 2020; Moslehi et al. 2020).

It is a real-time optimization problem to determine the ELL value based on the network components and constraints (operating variables, leakage management team characteristics and minimum and maximum limits of pressure), the requirements and constraints of prevention methods and their cost analysis. The optimization parameters in the ELL model are the definition of the optimum values of the system operating pressure and the number of the failure management teams. Therefore, there is a need for an optimization-based model that considers all factors, defines cost components and is based on cost–benefit analysis in order to define the ELL.

An optimization model, which considers the system and water loss components, the requirements, constraints and cost components of the methods and analyzes the ELL, was applied. This model determines the optimum values of pressure and the number of teams required to reach the ELL value. Basically, six variables – the useful life, cost of DMA implementation, potential economical recoverable leakage volume for PM, ALC, optimization of leakage management teams and water meter renewal – are calculated with the ELL model (Figure 1) (Firat et al. 2021). It would be more accurate to evaluate these different methods used to reduce water losses together. When the variables are defined together for each method in the ELL model, joint solutions of the system and the established algorithms become possible.
Figure 1

The flowchart for ELL analysis.

Figure 1

The flowchart for ELL analysis.

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It will not be possible to talk about water loss reduction methods and the ELL value for a system where the system's useful life was completed. Therefore, firstly, the useful life of the network should be calculated with the ELL model. The methods of PM and ALC are critical for the reduction of the water losses. However, the DMAs should be planned and implemented in the field in order to apply the PM and ALC methods. Therefore, the DMA implementation cost is considered a cost parameter in the ELL model.

In the ELL model, the ‘pressure value’ and ‘number of teams’ variables are optimized to determine the optimum value of losses at each iteration. The amount of NRW in a DMA is calculated using system input volume (Vinput) and authorized consumptions (Vcons.):
formula
(1)
formula
(2)
The objective functions, which are the multi-objective function, given in Equations (3) and (4) are defined. In those functions, the total loss value is minimized and benefits are maximized with the optimization algorithm. At this stage, the common variables for each water loss reduction method are evaluated together (for example, pressure is a parameter that is effective in more than one method) and the benefits are calculated separately. These calculated benefits represent the volume of water that will be saved to the system if the method is applied.
formula
(3)
formula
(4)
The multi-objective function defined in this study is analyzed with an algorithm in order to define the best-fit values of optimization parameters at the same time:
formula
(5)
In these equations, w1 and w2 are weights, which are adjusted according to the dynamics of the problem. A discrete stochastic optimization (DSO) algorithm is used to find the optimization parameters in the objective function. The DSO method performs fewer tests compared with the other heuristic methods and applies random increments/decrements to all coefficients simultaneously, allowing an infinite search direction (Firat et al. 2021; Yilmaz et al. 2021). Therefore, the DSO algorithm is applied for tuning of the optimum value of pressure and number of failure repair teams in ELL analysis. For this aim, a novel ELL model based on an optimization algorithm was developed by considering the unique objective function defined for the optimization of the two parameters, such as the optimum value of pressure and the number of repair teams. In this model, the optimization vector (OV), includes two optimization parameters, such as the optimum value of pressure and the number of repair teams (Equation (6)):
formula
(6)

In this study, the performance of the DSO method was also tested (as explained in Wang (2013)). The analysis steps of this algorithm can be given as follows (Firat et al. 2021):

  • Step 1: Set an initial value to the current point and apply to objective function (Equation (5)) .

  • Step 2: Generate a candidate solution () randomly and calculate .

  • Step 3: If then update solution .

  • Step 4: If , where ε (epsilon) is a tolerance, stop search process, else increment iteration (n = n + 1) and go to Step 2.

In this model, an initial value for the OV vector is given. Then, the vector value is obtained by applying the objective function and the candidate solutions are tuned with the algorithm. This analysis can be continued up to a certain value or the maximum number of iterations (Ates et al. 2020).

In this study, three utilities are considered as application areas. In the selection of the regions, attention was paid to the differences in the basic characteristics of the network (main length, water production cost, number of customers, water loss amounts, etc.) and operating characteristics (pressure, failure densities, meter failure rates, etc.) (Table 1). Thus, it is intended to reveal that the ELL can be different depending on the network and system characteristics. The data for ELL in utilities were obtained from SCADA, geographical information systems (GIS), customer information systems and failure management systems.

Table 1

Basic data for ELL analysis in pilot utilities

VariablesUnitUtility IUtility IIUtility III
Population No. 641,799 91,602 1,204,641 
Main length km 2,359 377 9,199 
Number of customers No. 254,588 35,475 550,208 
Number of service connections No. 80,382 7,095 76,839 
Average service connection length 15 
Operation pressure at minimum night flow 55 45 60 
Minimum operation pressure 20 35 20 
Maximum operation pressure 65 65 75 
System input flow rate m3/month 7,177,248 532,913 94,116,892 
Authorized billed consumption volume m3/month 3,066,742 265,062 59,594,097 
Unit water production cost TL/m3 0.76 10.02 1.66 
Unit water sales price TL/m3 5.82 6.37 5.64 
Ratio of meters over ten years old to total meters 12.42 0.28 – 
Is there a DMA approach in the region? Yes/No 
If DMA is available, total DMA network length km 329 175 7,080 
How many DMAs are there? No. 54 29 46 
Is pressure control performed in the DMAs? Yes/No 
Is flow measurement performed in the DMAs? Yes/No 
Is SCADA available on DMAs? Yes/No 
Total number of reported failures No. 11,188 1,812 16,242 
Total number of failure repair teams No. 12 27 
Average failure resolution time Hour/no. 31.43 14.64 22 
Current weighted pipe type of the network – PVC PVC PVC 
Percentage of pipe lengths: diameters of less than ø 150 mm 42 80 88.69 
Percentage of pipe lengths: between ø 150–300 mm diameters 39 14 10.45 
Percentage of pipe lengths: between ø 300–500 mm diameters 13 0.69 
Percentage of pipe lengths: between ø 500–700 mm diameters 0.10 
Percentage of pipe lengths: diameters of more than ø 700 mm 0.07 
New pipe type in the case of network renewal  PVC HDPE PVC 
VariablesUnitUtility IUtility IIUtility III
Population No. 641,799 91,602 1,204,641 
Main length km 2,359 377 9,199 
Number of customers No. 254,588 35,475 550,208 
Number of service connections No. 80,382 7,095 76,839 
Average service connection length 15 
Operation pressure at minimum night flow 55 45 60 
Minimum operation pressure 20 35 20 
Maximum operation pressure 65 65 75 
System input flow rate m3/month 7,177,248 532,913 94,116,892 
Authorized billed consumption volume m3/month 3,066,742 265,062 59,594,097 
Unit water production cost TL/m3 0.76 10.02 1.66 
Unit water sales price TL/m3 5.82 6.37 5.64 
Ratio of meters over ten years old to total meters 12.42 0.28 – 
Is there a DMA approach in the region? Yes/No 
If DMA is available, total DMA network length km 329 175 7,080 
How many DMAs are there? No. 54 29 46 
Is pressure control performed in the DMAs? Yes/No 
Is flow measurement performed in the DMAs? Yes/No 
Is SCADA available on DMAs? Yes/No 
Total number of reported failures No. 11,188 1,812 16,242 
Total number of failure repair teams No. 12 27 
Average failure resolution time Hour/no. 31.43 14.64 22 
Current weighted pipe type of the network – PVC PVC PVC 
Percentage of pipe lengths: diameters of less than ø 150 mm 42 80 88.69 
Percentage of pipe lengths: between ø 150–300 mm diameters 39 14 10.45 
Percentage of pipe lengths: between ø 300–500 mm diameters 13 0.69 
Percentage of pipe lengths: between ø 500–700 mm diameters 0.10 
Percentage of pipe lengths: diameters of more than ø 700 mm 0.07 
New pipe type in the case of network renewal  PVC HDPE PVC 
Table 2

Current condition of systems

ParametersUnitUtility IUtility IIUtility III
System input volume m3/day 239,241.60 17,763.80 3,137,230.00 
Authorized consumption m3/day 102,224.74 8,835.42 1,986,470.00 
NRW volume m3/day 137,016.86 8,928.38 1,150,760.00 
NRW rate 57.27 50.26 36.68 
Authorized unbilled consumption m3/day 16,368.90 621.73 470,584.46 
Authorized unbilled consumption 6.84 3.50 15.00 
Water loss volume m3/day 120,647.96 8,306.61 680,175.54 
Water loss rate 50.43 46.76 21.68 
Apparent loss volume m3/day 13,186.82 1,500.86 199,835.52 
Apparent loss rate 5.51 8.45 6.37 
Real loss volume m3/day 107,461.14 6,805.75 480,340.02 
Real loss rate 44.92 38.31 15.31 
ParametersUnitUtility IUtility IIUtility III
System input volume m3/day 239,241.60 17,763.80 3,137,230.00 
Authorized consumption m3/day 102,224.74 8,835.42 1,986,470.00 
NRW volume m3/day 137,016.86 8,928.38 1,150,760.00 
NRW rate 57.27 50.26 36.68 
Authorized unbilled consumption m3/day 16,368.90 621.73 470,584.46 
Authorized unbilled consumption 6.84 3.50 15.00 
Water loss volume m3/day 120,647.96 8,306.61 680,175.54 
Water loss rate 50.43 46.76 21.68 
Apparent loss volume m3/day 13,186.82 1,500.86 199,835.52 
Apparent loss rate 5.51 8.45 6.37 
Real loss volume m3/day 107,461.14 6,805.75 480,340.02 
Real loss rate 44.92 38.31 15.31 

The current levels of water losses in the utilities were defined using these data (Table 2). The NRW rates in utilities vary between 36% and 57% and water loss rates vary between 21% and 50%. It is obvious that water loss reduction studies should be implemented in these regions. However, in these utilities, choosing the most appropriate and efficient method for reducing losses is the most important issue to be decided. The factors causing water losses in the networks may be different according to the characteristics of the utilities. The fluctuations in the pressure levels in the regions are expected to significantly affect the current losses and the selection of prevention methods. Apparent and real loss rates in the regions are also important parameters to be considered. For this reason, understanding the current losses plays an important role in determining the methods to be developed, but it does not provide a sufficient argument on its own. More importantly, it is necessary to decide the economically recoverable volume of losses by applying these methods. The ELL values were determined in pilot utilities based on the optimization model detailed in the previous sections (Table 3). In addition, the optimum values of operation pressures and number of teams were defined by the optimization algorithm based on the network characteristics and the current state of the utilities (Table 3, Figure 2).
Table 3

ELL model results for pilot utilities

ParametersUtility I
Utility II
Utility III
Current valueOptimization resultCurrent valueOptimization resultCurrent valueOptimization result
Pressure 55 46.36 45 31.5 60 29.6 
Number of teams No. 12 13 27 29 
Average failure resolution time Hour/no. 31.43 29.01 14.64 13.44 22 17.41 
NRW volume m3/day 137,016.86 69,369.86 8,928.38 2,906.38 1,150,760 711,855.00 
NRW rate 57.27 29.00 50.26 16.36 36.68 22.69 
Water loss volume m3/day 120,647.96 53,000.95 8,306.61 2,284.61 680,175.54 241,270.54 
Water loss rate 50.43 22.15 46.76 12.86 21.68 7.69 
ParametersUtility I
Utility II
Utility III
Current valueOptimization resultCurrent valueOptimization resultCurrent valueOptimization result
Pressure 55 46.36 45 31.5 60 29.6 
Number of teams No. 12 13 27 29 
Average failure resolution time Hour/no. 31.43 29.01 14.64 13.44 22 17.41 
NRW volume m3/day 137,016.86 69,369.86 8,928.38 2,906.38 1,150,760 711,855.00 
NRW rate 57.27 29.00 50.26 16.36 36.68 22.69 
Water loss volume m3/day 120,647.96 53,000.95 8,306.61 2,284.61 680,175.54 241,270.54 
Water loss rate 50.43 22.15 46.76 12.86 21.68 7.69 
Figure 2

Results of the ELL in pilot utilities.

Figure 2

Results of the ELL in pilot utilities.

Close modal

The economic levels of NRW rates are 29% for System I, 16% for System II and 23% for System III. The economic level of NRW values varies between 16% and 30% in utilities, depending on the basic network and operating characteristics of the regions. Similarly, economic water loss rates were determined as 22% for System I, 16% for System II and 8% for System III.

The main reason for the differences in the economic levels of NRW rates determined in the pilot utilities is that the costs and benefits of the loss reduction methods and the current status of the networks differ, especially in System I, where water production costs are lower than in other systems. The fact that the economic level of NRW value is 29% reveals the effect of water production cost. It is seen that the investments made to reduce water losses in this utility will not provide economic efficiency after a certain point.

On the other hand, in System II, where the water production cost is quite high, the economic level of NRW was calculated as 16%. The methods applied to prevent leaks will provide more benefits for the utility due to the high cost of water production. Accordingly, the current situation of the utilities and the water production costs are effective in the creation of water loss reduction plans in the systems. Pressure and team optimization graphs for pilot utilities are given in Figures 3 and 4. In these figures, A0 is the optimum value of pressure (m), A1 is the optimum number of failure repair teams (no.) and the x-axis represents the number of iterations of the algorithm.
Figure 3

Pressure optimization results: (a) System I, (b) System II and (c) System III.

Figure 3

Pressure optimization results: (a) System I, (b) System II and (c) System III.

Close modal
Figure 4

Failure repair team optimization results: (a) System I, (b) System II and (c) System III.

Figure 4

Failure repair team optimization results: (a) System I, (b) System II and (c) System III.

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In System I, the operating pressure should be reduced from 55 to 46.36 m according to the results of the optimization model. In addition, this model suggests to increase the number of failure repair teams to 13 for effective leak management in this system. Similarly, operating pressure should be reduced from 45 to 31.5 m for leak management in System II. It is determined that the number of teams in this system is sufficient and should be kept constant. On the other hand, it is suggested by the model to reduce the operating pressure from 60 to 29.6 m in System III. It was also suggested that the number of leakage management teams should be increased to 29 for leak repair and management in this region.

The necessity of PM in all systems was revealed as a result of the analysis. It is understood that the average pressure of the regions should be reduced between 20% and 50%. It was observed that significant water losses can be reduced just by controlling the pressure in these systems, because the reduction in pressure not only reduces the losses due to existing failures but also has the ability to prevent possible losses by reducing the frequency of failure. Another water loss reduction method is the ALC method (covering the leak monitoring and detection). The results show that the ALC method will provide significant advantages in every system. This method, which gives more successful results in regions where the subscriber density per km is high and the pressure is above average, gives successful results in all three regions. The total benefits (reductions in seepage) obtained for all three utilities with the ALC method were calculated (Table 3 and Figure 2).

Management of failure repair teams is also an important issue for utilities. Although the establishment and operation of each team create a constant cost, it provides a serious advantage by shortening the failure resolution times in the failure management strategy. Shortening fault resolution times both reduces water losses and increases customer satisfaction level and service quality. For this reason, determining the ideal number of teams is necessary due to the influence of the economic and social effects in water management. The results showed that the number of teams in Systems I and III was insufficient and should be increased. In System II, the number of existing teams is optimum.

The application of the most basic methods in the field was foreseen in order to reach the economic levels of NRW volume and rate calculated by the optimization algorithm. The potential benefits (reduction in the amount of loss) that can be obtained depending on the implementation of basic water loss prevention methods are calculated (Table 4).

Table 4

Total savings for main methods in the ELL model

MethodUnitUtility IUtility IIUtility III
Total savingsTotal savingsTotal savings
Pressure management m3/day 24,996 2,962 296,000 
Active leakage control m3/day 35,234 3,060 118,000 
Water meter management m3/day 5,139 – – 
Team management m3/day 2,278 – 24,905 
Total 67,647 6,022 438,905 
MethodUnitUtility IUtility IIUtility III
Total savingsTotal savingsTotal savings
Pressure management m3/day 24,996 2,962 296,000 
Active leakage control m3/day 35,234 3,060 118,000 
Water meter management m3/day 5,139 – – 
Team management m3/day 2,278 – 24,905 
Total 67,647 6,022 438,905 

In System II, it is understood that no benefit can be obtained with team management since the number of failure repair teams remains constant as a result of optimization. Moreover, no benefit will be gained by replacing meters in this system due to the low rate of meters (0.28%) older than ten years in the current situation in this utility. In this system, the amount of leakage that can be saved by ALC is close to the value obtained from PM.

It is estimated that 296,000 m3/day leakage will be prevented by reducing the operating pressure from the current 60 to 29.6 m in System III. The most appropriate pressure level was determined in this system by considering the characteristics of the region, customer consumption behaviors, number of critical points and pressure requirements, regulation limit values, ALC and leakage management requirements. It is also understood that significant leaks can be prevented in this region depending on the application of the ALC method.

The method in which the most benefit is obtained in System I has been determined as ALC. Leakages can be reduced significantly depending on the implementation of PM in this system. The results show that the benefits to be obtained from water loss prevention methods may differ depending on the characteristics of the network. According to these results, before applying water loss prevention methods in distribution systems, current network conditions should be analyzed and potential benefits should be estimated. Moreover, the current network conditions should be considered to define the target for the water loss level in the system.

Table 5

The cost and benefits in pilot utilities

MethodUnitOptimization resultCostBenefitDifference (benefit–cost)
System I 
DMA No. A total of 64 DMAs should be created. ₺5,621,400.00 ₺0.00 –₺5,621,400.00 
PM m3/day 24,996 ₺0.00 ₺6,933,890.40 ₺6,933,890.40 
ALC m3/day 35,234 ₺8,280,000.00 ₺9,773,911.60 ₺1,493,911.60 
Water meter management m3/day 5,139 ₺5,691,570.00 ₺21,833,555.40 ₺16,141,985.40 
Team management m3/day 2,278 ₺420,000.00 ₺631,917.20 ₺211,917.20 
Total 67,647 ₺20,012,970.00 ₺39,173,274.60 ₺19,160,304.60 
System II 
DMA No. A total of 14 DMAs should be created. ₺2,555,000.00 ₺0.00 –₺2,555,000.00 
PM m3/day 2,962 ₺0.00 ₺10,832,922.60 ₺10,832,922.60 
ALC m3/day 3,060 ₺5,810,000.00 ₺11,191,338.00 ₺5,381,338.00 
Water meter management m3/day ₺0.00 ₺0.00 ₺0.00 
Team management m3/day ₺0.00 ₺0.00 ₺0.00 
Total 6,022 ₺8,365,000.00 ₺22,024,260.60 ₺13,659,260.60 
System III 
DMA No. A total of 198 DMAs should be created. ₺21,923,460.00 ₺0.00 –₺21,923,460.00 
PM m3/day 296,000 ₺425,000.00 ₺179,346,400.00 ₺178,921,400.00 
ALC m3/day 118,000 ₺11,140,000.00 ₺71,496,200.00 ₺60,356,200.00 
Water meter management m3/day ₺0.00 ₺0.00 ₺0.00 
Team management m3/day 24,905 ₺840,000.00 ₺15,089,939.50 ₺14,249,939.50 
Total 438,905 ₺34,328,460.00 ₺265,932,539.50 ₺231,604,079.50 
MethodUnitOptimization resultCostBenefitDifference (benefit–cost)
System I 
DMA No. A total of 64 DMAs should be created. ₺5,621,400.00 ₺0.00 –₺5,621,400.00 
PM m3/day 24,996 ₺0.00 ₺6,933,890.40 ₺6,933,890.40 
ALC m3/day 35,234 ₺8,280,000.00 ₺9,773,911.60 ₺1,493,911.60 
Water meter management m3/day 5,139 ₺5,691,570.00 ₺21,833,555.40 ₺16,141,985.40 
Team management m3/day 2,278 ₺420,000.00 ₺631,917.20 ₺211,917.20 
Total 67,647 ₺20,012,970.00 ₺39,173,274.60 ₺19,160,304.60 
System II 
DMA No. A total of 14 DMAs should be created. ₺2,555,000.00 ₺0.00 –₺2,555,000.00 
PM m3/day 2,962 ₺0.00 ₺10,832,922.60 ₺10,832,922.60 
ALC m3/day 3,060 ₺5,810,000.00 ₺11,191,338.00 ₺5,381,338.00 
Water meter management m3/day ₺0.00 ₺0.00 ₺0.00 
Team management m3/day ₺0.00 ₺0.00 ₺0.00 
Total 6,022 ₺8,365,000.00 ₺22,024,260.60 ₺13,659,260.60 
System III 
DMA No. A total of 198 DMAs should be created. ₺21,923,460.00 ₺0.00 –₺21,923,460.00 
PM m3/day 296,000 ₺425,000.00 ₺179,346,400.00 ₺178,921,400.00 
ALC m3/day 118,000 ₺11,140,000.00 ₺71,496,200.00 ₺60,356,200.00 
Water meter management m3/day ₺0.00 ₺0.00 ₺0.00 
Team management m3/day 24,905 ₺840,000.00 ₺15,089,939.50 ₺14,249,939.50 
Total 438,905 ₺34,328,460.00 ₺265,932,539.50 ₺231,604,079.50 

In this study, benefit and costs were calculated separately for the basic methods (Table 5). In this context, water production cost was used when calculating the benefits obtained with real losses and water sales prices were used when calculating the benefits and costs obtained with apparent losses. It is obvious that the utilities can gain significant benefits and increase water service quality and satisfaction with the application of water loss reduction methods. The results play a significant guiding role for utilities in calculating the economic levels of NRW.

In this study, ELL values were determined by the optimization algorithm for utilities with different network characteristics, water production and operating costs and institutional capacities. The NRW rates in these utilities are currently calculated as 57%, 50% and 37%, respectively. ELL values were analyzed by the optimization model to define the most appropriate water loss prevention strategy in regions with very high loss rates. The economic levels of the NRW in the pilot utilities were calculated as 29%, 16% and 23% by the optimization algorithm. Moreover, the most appropriate reduction methods to be applied according to the conditions of the utilities were determined in order to reach these defined ELL values. The potential benefits (reductions in the amount of losses) to be obtained in each utility condition were calculated with the application of these methods. In general, it has been seen that serious benefits can be obtained by applying the basic water loss reduction methods (PM, ALC, team management and water meter management). In the study, it was determined by the optimization algorithm that the PM and ALC methods are the most effective water loss reduction methods depending on different network characteristics. On the other hand, it was seen that methods of failure repair team management and water meter management will not always be economically beneficial for utilities. Therefore, the necessity of calculating the benefit costs according to the current conditions of the network was put forward before applying such methods. At the same time, ideal pressure levels were calculated for each region and it should not be forgotten that these pressures give the most useful results in the use of both ALC and PM methods.

Utilities are making quite a serious effort to reduce NRW volumes and rates. In general, the target values of NRW rates in utilities are defined with the regulations. Such targets cause many uneconomical and serious problems encountered in practice for utilities. It is possible to develop the most appropriate WLM strategy for utilities by the optimization model, which can be applied to any network where the data given in the table are measured. The main important issue in the implementation of this model is reliable and accurate data. The basic data of the system should be determined correctly for an accurate analysis. In addition, regular monitoring of operation and hydraulic data with information management systems such as SCADA and GIS is quite important. Especially, the cost of water production is a factor that directly affects the definition of the reduction method and ELL value. The definition of the reduction methods to reach the defined ELL is significant information for technical staff in utilities. It is thought that utilities can continue their loss reduction strategies by applying this model that will provide the most economical efficiency. The scope of the study can be expanded by considering new optimization parameters in the future for the algorithm. Four methods (PM, PMM, ALC and failure management) were considered as water loss reduction methods. In future studies, different water loss reduction methods can be integrated into the algorithm.

This research was supported by TUBITAK (Turkish National Science Foundation) under the Project Number 122M577.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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