Increasing water demands, in parallel with water scarcity, force water utilities in developing countries to follow complex operation techniques for the distribution of the available amounts of water. The reduction of water losses from water supply systems can help fill the gap between supply and demand. This strategy is characterized by the variety of options, and identification of the most appropriate option is a demanding process. An integrated multi-criteria decision making framework, precisely analytic hierarchy process (AHP) is applied to prioritize water loss reduction options for a typical intermittent water supply network in Palestine. The AHP was applied to structure the decision problem into a hierarchy framework, and four groups of decision makers were engaged to evaluate the elements. The results show that the strategic plan, which comprises controlling and managing operational pressure, was the best option. The selection of this option was highly connected to the conditions of the case study and to the well-known advantages of this strategy to reduce losses. It is anticipated that the developed methodology will help water utilities to identify integrated water loss management plans that will guide utility managers to conduct better control for water losses.

INTRODUCTION

Water losses from water distribution systems (WDSs) have a significant impact on the economic sustainability of urban water utilities, and also represent a most relevant indicator of their inefficiency (Loureiro et al. 2015; Vilanova et al. 2015). Its control promotes the efficient use of water as a valuable natural resource by allowing for less water to be collected from the environment (Loureiro et al. 2015). In urban areas, the leaks from WDSs often provide substantial groundwater recharge, which partially counterbalances the lack of inflow because infiltration is reduced as a result of the presence of paved areas and buildings (Ducci & Sellerino 2015). Controlling water losses has been widely tackled and discussed by researchers in recent years in light of its high significance in the performance of WDSs (Walski et al. 2006; Mutikanga et al. 2011; Morais et al. 2014).

All water systems are vulnerable to water losses. It is only the volume of water losses that varies (Farley & Trow 2003). Water losses can be classified as real and apparent losses, where leakage represents the main component of the real losses and measurement errors and unauthorized consumption represent the apparent losses (Morais et al. 2014). The significant amount of water losses is related to leakage, which can be caused by different factors such as poor pipe connections, aging of pipes, ground movement, high pressure, and poor quality of workmanship (Puust et al. 2010). It varies from 3% in well-structured and managed systems to over 50% in poorly managed systems (Puust et al. 2010).

The operation of water systems in intermittent mode is one of the most common techniques employed to address the shortfall between supply and demand and is followed in cases of water scarcity as there is insufficient water supply available to meet all demands 24 hours per day, 7 days per week. It has been widely reported that the majority of water systems in developing countries are intermittent, and it is also broadly adopted in developed countries faced with water scarcity (Vairavamoorthy et al. 2001; Abu-Madi & Trifunovic 2013; Nyende-Byakika et al. 2013).

In water scarcity conditions, this practice contributes to reducing background water losses, as restrained supply hours can limit leakage (Criminisi et al. 2009; De Marchis et al. 2010). The prolonged use could cause opposite effects: the rate of deterioration of pipes will increase due to water hammer, which is a hydraulic transient shock caused by opening or closing pumps and valves (Wang et al. 2014), stimulated by the filling and emptying of the pipes (De Marchis et al. 2010). The pressure fluctuations will increase the rate of bursts and leakages (Freni et al. 2014).

By reviewing the literature, there are two principal strategies to improve the intermittent service: optimizing the intermittent scheme, which comprises planning guidelines and design tools to provide equitable distribution of the limited quantity of water, and developing models to simulate the intermittent operation in reality, since the conventional methods, demand-driven approaches developed for continuous supply systems, are not suited for the analysis of intermittent systems (Totsuka et al. 2004). The other strategy suggests stepwise introduction of continuous distribution (Klingel & Nestmann 2013). The minimizing of water supply losses could be an introduction to allow continuous and equitable water distribution in intermittent systems by exploiting recovered water to balance the water demands.

There are a wide range of options to manage and reduce water losses, among them the use of advanced techniques such as online monitoring, and sensors with multi-parameter measurements, control of pressure and asset management. The process as a whole is sophisticated, costly, requires skilled staff, demands different levels of coordination, and comprises different interests of stakeholders (Mutikanga et al. 2011). To avoid the complexity of this multiple criteria task, multi-criteria decision analysis (MCDA) techniques are proposed.

In this paper we show the application of MCDA based on participatory interaction approaches by influential actors in the water sector in Palestine, with the purpose of selecting the most appropriate solutions to reduce and manage water losses in intermittent water supply systems and making the best consensus decisions in a complex environment.

METHODOLOGY

Multi-criteria decision analysis

MCDA is a framework used for sorting or scoring the overall performance of decision options against multiple objectives (Hajkowicz & Collins 2007). It was developed in the field of decision theory to resolve issues related to operational research, and is characterized by a finite number of decision alternatives (Zopounidis & Doumpos 2002).

In the field of water resources management, MCDA techniques have a strong ability to produce well structured, consistent, and objective solutions to complex decision issues (Lai et al. 2008). The most used in this field, as indicated by Hajkowicz & Collins (2007), are fuzzy set analysis, compromise programming (CP), analytic hierarchy process (AHP) (Saaty 1980), elimination and choice translating reality (ELECTRE), and the preference ranking and organization method for enrichment evaluation (PROMETHEE) (Hajkowicz & Collins 2007). Several comparative studies of MCDA techniques on a single problem in water resources management showed that the results were often in close agreement, and the most important aspect is the selection of criteria and options and structuring the decision problem (Hajkowicz & Collins 2007). Therefore, the focus will be on identifying the MCDA technique with a potential to structure the problem in an understandable fashion.

Analytic hierarchy process

AHP is one of the extensively used pairwise comparison techniques applied in the field of multi-attribute decision making (Hajkowicz & Collins 2007). It was selected due to its clear mathematical properties, the ease of obtaining the required input data, and its effectiveness in handling qualitative and quantitative data and structuring the complex decision problems to multi-level structures. This advantage of breaking down the complex problem into its components assists with the tradeoff among the components. The AHP procedure starts with formulation of the decision problem in the form of a hierarchical structure with several levels, followed by a prioritization step in the form of a decision matrix, which is a square matrix as shown below, to calculate the importance of the elements by executing pairwise comparison among the elements in each level using positive entries according to the numerical scale in Table 1 (Şener et al. 2010): 
formula
1
A is the decision matrix, aij are pairwise comparisons between elements i and j for all i,j{1, 2,……, n}

It is possible to derive relative weights of the elements of each level by following the technique of averaging over normalized columns to estimate the eigenvalues (Bascetin 2007).

To measure the accuracy of the pairwise comparisons, the error due to inconsistency should be measured by calculating the consistency index (CI) and the consistency ratio (CR) (Delgado-Galván et al. 2010): 
formula
2
where is the principal eigenvalue, and n is the order of the matrix. The CI can then be compared to that of a random consistency index (RI), which is displayed in Table 2.
Table 1

Saaty numerical scale for pairwise comparisons in AHP

Judgment termSaaty (aij)
Absolute preference (element i over element j
Very strong preference (i over j
Strong preference (i over j
Weak preference (i over j
Indifference as regards i and j 
Weak preference (j over i1/3 
Strong preference (j over i1/5 
Very strong preference (j over i1/7 
Absolute preference (j over i1/9 
When compromise is needed – intermediate values 2,4,6,8…..1/2,1/4, 1/6, 1/8 
Judgment termSaaty (aij)
Absolute preference (element i over element j
Very strong preference (i over j
Strong preference (i over j
Weak preference (i over j
Indifference as regards i and j 
Weak preference (j over i1/3 
Strong preference (j over i1/5 
Very strong preference (j over i1/7 
Absolute preference (j over i1/9 
When compromise is needed – intermediate values 2,4,6,8…..1/2,1/4, 1/6, 1/8 
Table 2

Saaty values of random consistency

Matrix size (n)12345678910
Random CI (RI0.00 0.00 0.52 0.89 1.11 1.25 1.35 1.40 1.45 1.49 
Matrix size (n)12345678910
Random CI (RI0.00 0.00 0.52 0.89 1.11 1.25 1.35 1.40 1.45 1.49 
The CR for the same order matrix is given by the following equation: 
formula
3
As a general rule, CR ≤ 10% should be maintained for the matrix to be consistent (Şener et al. 2010). In the case of CR values larger than 10%, the inconsistency level should be improved by reducing or augmenting the most inconsistent value to the next evaluation point step by step (Calizaya et al. 2010). The proposed procedure to improve inconsistent judgments in case they arise is the use of an AHP priority calculator, in which it is possible to improve the three most inconsistent judgments in one iteration (BPMSG 2015). The composite weights can be derived by aggregating the weights through the hierarchy structure.

Despite the effectiveness of AHP, there is a degree of uncertainty associated with judgments, the exact numerical values used to express the preferences of decision makers (DMs). This uncertainty is propagated through the hierarchy structure and could result in lowering the confidence in the final outputs, which are not necessarily precise (Eskandari & Rabelo 2007). Different methods have been developed to gain insights into this issue such as the stochastic approach proposed by Eskandari & Rabelo (2007), which is based on calculating the variances of the final weights and establishing confidence intervals. Since the more inconsistent judgment ratios in the pairwise comparisons will lead to more uncertainty (Eskandari & Rabelo 2007), the procedure for improving the inconsistency as explained previously will be followed.

The methodology that integrates the principles of MCDA techniques to prioritize the best options that satisfy the different decision criteria of water loss management strategies will be initiated by defining the objectives and overall goal, developing the hierarchy structure by identifying a set of criteria, evaluation criteria, and options, and decomposition of the decision problem into decision elements. This will be followed by engaging DMs to make subjective comparisons among decision elements, checking and improving the consistency of the preferences, deriving the relative weights of elements for each level, and obtaining the overall ratings of options by aggregating the weighted decision elements. The illustration of the proposed methodology is depicted in Figure 1. More details will be explained in the following sections.
Figure 1

Flowchart of the MCDA model.

Figure 1

Flowchart of the MCDA model.

STRUCTURING THE DECISION PROBLEM

Overall goal

The overall goal of this study is to build an integrated framework of strategies as a base to identify and prioritize options to reduce water losses in a typical intermittent water system. The adopted strategy should be able to satisfy a set of objectives compatible with the general policy of the water sector in the targeted area such as maximizing the reliability of supply, improving the quality of water, increasing the rate of water savings, maintaining an affordable water supply and reducing the costs.

Identification of decision makers

Since the management of water losses is typically a complex decision-making process involving many objectives and perspectives, the interests of other stakeholders in addition to the needs of the responsible authorities should be considered to build a well-structured framework characterized by rationality and transparency. This stage comprises the involvement of experts who have an in-depth understanding of the decision problem.

Identification of options

This phase comprises the identification of alternatives that affect the overall goal. The selected options, which are displayed in Table 3, were derived from a literature survey of recently available literature on water loss management (Alegre et al. 2006; AWWA 2009; Delgado-Galván et al. 2010; Mutikanga et al. 2011; Morais et al. 2014), consultation with experts and DMs, and based on the local conditions of the study area.

Table 3

Proposed options

CodeOptionExplanations
A1 Active leakage control Taking actions to identify and repair leaks that have not been reported (proactive control) 
A2 Passive leakage control Repairing reported or evident leaks only, (reactive control) 
A3 Operational pressure control & management Managing system pressures to the optimum levels of service ensuring sufficient and efficient supply to customers, while reducing unnecessary or excess pressures 
A4 Establishing district metering areas (DMAs) Useful for improving the management of the network, monitoring the input and output discharges, DMAs have more homogenous characteristics 
A5 Asset management for service lines & selective mains Replacement of affected service lines and mains 
A6 Replacement of water meters To eliminate water meter inaccuracy, may include installation of automatic water meters 
A7 Improving quality level of repairs & minimizing the required time of repairing To prevent the recurrence of bursts, and to minimize the adverse effects of disruption of the service 
A8 Control of illegal use of water service To eliminate the illegal use of water through public awareness campaigns, and effective monitoring 
A9 Utilizing advanced techniques to fix & predict the leakage Includes concrete database, hydraulic and GIS models, failure prediction models, installing pressure sensors, noise and flow sensors 
A10 Public awareness & educational campaigns Useful to accelerate the process of fixing and repairing visible leakage and bursts, also useful for water conservation by addressing the losses after consumer water meters 
CodeOptionExplanations
A1 Active leakage control Taking actions to identify and repair leaks that have not been reported (proactive control) 
A2 Passive leakage control Repairing reported or evident leaks only, (reactive control) 
A3 Operational pressure control & management Managing system pressures to the optimum levels of service ensuring sufficient and efficient supply to customers, while reducing unnecessary or excess pressures 
A4 Establishing district metering areas (DMAs) Useful for improving the management of the network, monitoring the input and output discharges, DMAs have more homogenous characteristics 
A5 Asset management for service lines & selective mains Replacement of affected service lines and mains 
A6 Replacement of water meters To eliminate water meter inaccuracy, may include installation of automatic water meters 
A7 Improving quality level of repairs & minimizing the required time of repairing To prevent the recurrence of bursts, and to minimize the adverse effects of disruption of the service 
A8 Control of illegal use of water service To eliminate the illegal use of water through public awareness campaigns, and effective monitoring 
A9 Utilizing advanced techniques to fix & predict the leakage Includes concrete database, hydraulic and GIS models, failure prediction models, installing pressure sensors, noise and flow sensors 
A10 Public awareness & educational campaigns Useful to accelerate the process of fixing and repairing visible leakage and bursts, also useful for water conservation by addressing the losses after consumer water meters 

The proposed options are generally developed for water loss management in continuous water supply systems. Specific arrangements have to be considered to adapt options that are limited to use in continuous water supply systems, such as active leakage control. These arrangements comprise isolating the targeted area, supplying water in a continuous manner during the leak detection campaigns, and using leak detection equipment to locate the leaks (Farely 2001).

Some of the options are more related to the case of intermittent supply and the boundary conditions of the case study. As the intermittent scheme of operation is common in resource-constrained utilities, the use of poor quality materials (i.e. water meters and fittings) is common (Kumpel & Nelson 2016). Overrating pumping facilities to supply customers in accordance with their needs during the limited supply periods and to overcome the high elevation problems (i.e. the conditions of the case study, where there are considerable differences in elevation) increases the malfunction of water meters and breaks in service connections. Accordingly, there is a need to install water meters with good quality and greater accuracy.

A public awareness option is advantageous, as most of the customers in intermittent water supply systems are forced to use ground/roof storage tanks to collect their needs during supply periods. Most of these tanks are equipped with float valves, which are mostly not well operated. Furthermore, illegal connections are common in intermittent systems (Kumpel & Nelson 2016). Therefore, addressing these issues is of benefit in eliminating water losses.

Identification of evaluation criteria

The evaluation criteria (EC) will be used to measure the performance of each option, and will lead finally to select the most appropriate option. Their importance should be clear to be able to identify the options, as shown in Table 4. They are also derived from a literature survey (Alegre et al. 2006; AWWA 2009; Delgado-Galván et al. 2010; Mutikanga et al. 2011; Morais et al. 2014).

Table 4

Proposed EC

CodeECObjective class-categoryPreference trendExplanations
E1 Generation of revenue Economic Maximize If the option contributes to producing and increasing revenue 
E2 Cost figure Economic Minimize Associated costs to implement the option 
E3 Operation and maintenance costs Economic Minimize Costs associated with control and upkeep of the option 
E4 Benefit period Economic Maximize Measuring the useful life span of the option 
E5 Water preservation and reduction of waste Environmental Maximize If the option has the ability to maximize water savings and minimize the pressure on natural resources 
E6 Energy saved Environmental Maximize If the option has the ability to minimize the energy consumption and greenhouse gas emissions 
E7 Supply reliability Technical Maximize If the option has the ability to save most continuous service and minimize supply interruptions 
E8 Flexibility Technical Maximize If the option has the capability of being adjusted to meet varied needs and uncertainties 
E9 Affordability Socio-economic Maximize Measuring the impact of option on level of water tariff 
E10 Water quality Socio-economic Maximize Measuring the ability of option to improve water quality 
CodeECObjective class-categoryPreference trendExplanations
E1 Generation of revenue Economic Maximize If the option contributes to producing and increasing revenue 
E2 Cost figure Economic Minimize Associated costs to implement the option 
E3 Operation and maintenance costs Economic Minimize Costs associated with control and upkeep of the option 
E4 Benefit period Economic Maximize Measuring the useful life span of the option 
E5 Water preservation and reduction of waste Environmental Maximize If the option has the ability to maximize water savings and minimize the pressure on natural resources 
E6 Energy saved Environmental Maximize If the option has the ability to minimize the energy consumption and greenhouse gas emissions 
E7 Supply reliability Technical Maximize If the option has the ability to save most continuous service and minimize supply interruptions 
E8 Flexibility Technical Maximize If the option has the capability of being adjusted to meet varied needs and uncertainties 
E9 Affordability Socio-economic Maximize Measuring the impact of option on level of water tariff 
E10 Water quality Socio-economic Maximize Measuring the ability of option to improve water quality 

Building the hierarchy structure

Figure 2 displays the hierarchy structure of the water loss management decision problem, which consists of four levels. The first level presents the overall goal, the second level presents criteria, the third level presents the sub-criteria or EC, which will be used as an index level to measure the performance of options, and the fourth level presents the options that should be evaluated and prioritized. The development of the hierarchy structure is the most important stage of the AHP technique, and a correct hierarchy structure is key to successful application.
Figure 2

Hierarchy structure of the proposed water loss management framework.

Figure 2

Hierarchy structure of the proposed water loss management framework.

Building the decision matrices

To carry out the MCDA model, which is displayed in Figure 2, three levels of decision matrices were developed. Each entry represents the evaluation of preferences of one element over the other with respect to achieving the goal in the above level.

The fundamental matrices comprise all necessary pairwise comparisons at each level. This process, which is a technical activity, has been accomplished by an expert team after a thorough discussion related to the proposed methodology and the hierarchy structure layout. The matrices were arranged according to the following form:

  • Matrix M1 represents the evaluation of the set of criteria (economic, environmental, technical and socio-economic) that are shown in level 2 of Figure 2.

  • Matrices M2.1–M2.4 represent the evaluation of sub criteria with respect to their own criterion in the upper level, e.g. the sub-criteria under the economic criterion that include generation of revenue, cost figures, operation and maintenance costs, and benefit period, will be evaluated with respect to the economic criterion.

  • Matrices M3.1–M3.10 represent the evaluation of the performance of options. They are illustrated in the last level of the hierarchy structure and explained in detail in Table 3 with codes from A1 to A10, against the EC. The set of ten matrices, M3.1–M3.10, will be generated by performing tradeoffs based on pairwise comparisons among the ten options, A1–A10, with respect to each of the ten EC, E1–E10, in level 3 of Figure 2. Each input in these decision matrices represents the relative importance of one option over another for a given evaluation criterion.

After building the decision matrices, the AHP methodology will be applied to derive the weights of elements at each level, and the consistency of results will be checked for all levels.

Aggregation of the outputs

The global weights can be derived by aggregating the weights through the hierarchy structure. To aggregate the preferences of all groups of DMs, two methods will be employed. The first is the geometric mean method (GMM), in which the geometric means for the individual preferences are used as elements in the matrices of the pairwise comparison, and then prioritized and their weights derived (Contreras et al. 2008), while the second uses the weighted arithmetic mean method (WAMM) (Ramanathan & Ganesh 1994), in which the priorities are computed and then combined using the WAMM. The process of aggregation of the individual/group preferences works in such a trend that the whole groups become a new individual by combining individual opinions to obtain a single opinion (Ramanathan & Ganesh 1994).

Sensitivity analysis

The sensitivity analysis was carried out by employing the capabilities of the Expert Choice Software (Expert Choice 2014) to examine the sensitivity of results to the changes in the weights of the criteria. This analysis is an important issue, since the outputs of the AHP method are based on subjective expert assessments. The sensitivity analysis surveyed the stability intervals in which changes in the criterion weight will not affect the ranking or the alternative hierarchy (Hsu et al. 2008). The weights for the four nodes under the overall goal (economic, environmental, technical, and socio-economic criteria) were gradually modified by varying their weights to observe the impact on the weights and rankings of options in the last level of the hierarchy structure.

Case study

The proposed methodology was applied to the Nablus water distribution system (NWDS), Nablus, Palestine. This city has a population of nearly 150,000 (PCBS 2015), and during much of the year there is not enough drinking water available to meet the demands of the people. Generally, no zone has a continuous water supply, and the average rate of water losses in 2012 registered as 38% of the total input (ANNU 2014).

For 2014, monthly water balance data were available from the Water and Sanitation Department in Nablus Municipality (WSSD), which allowed a calculation of non-revenue water (NRW). These calculations have shown ranges of monthly NRW between 35 and 44%.

Four groups of the most influential actors in the water sector in Palestine were involved in evaluating the water loss management strategy. They were:

DM1: Policy makers, demonstrated by representatives from the Palestinian Water Authority (PWA), who have full responsibility for managing water and wastewater resources in Palestine. The actors within this group have been chosen from the Economic and Tariff department at PWA. This department is responsible for preparing economic and social studies related to the water sector and establishing a unified water tariff system (PWA 2015).

DM2: Actors from the Water Control Directorate at PWA. This department is responsible for monitoring the exploitation of water resources, and making sure that they are exploited in an optimal way that guarantees the sustainability of the water resources and prevents its depletion and pollution (PWA 2015).

DM3: Environmental groups, represented by the Palestinian Hydrology Group (PHG), which is the largest Palestinian non-governmental organization working to improve access to water and sanitation services, and to monitor pollution and climate change in the Palestinian territories (PHG 2015).

DM4: The operator of the water supply system represented by WSSD, Nablus. This department renders two vital services: water supply and sanitation, and it serves more than 160,000 inhabitants in Nablus including surrounding villages, and is managed by a total of 289 employees (Municipality 2015).

RESULTS

The results of preferences of DMs were illustrated in the form of decision matrices. The following matrix shows, as an example, the output of preferences of the DM4 group, which represents the interests of operators of the water supply system for level 2 of Figure 2: 
formula
For the sub-criteria level, the matrices below illustrate the results of the preferences of DM4 related to the elements of sub-criteria, level 3 of Figure 2: 
formula
The preferences of DM4 for M3.2 in the last level of Figure 2 have been illustrated to reflect their preferences among the ten proposed options with respect to the cost figures sub-criteria in the third level of Figure 2, which has a code E2 in Table 4: 
formula

The consistency results for the decision matrices in the second and third levels of Figure 2 were acceptable and within Saaty's suggestions (CR ≤ 10%). For DM1, CR values were (4.5%–M1, 4.3%–M2.1), for DM2 (7.0%–M1, 4.03%–M2.1), for DM3 (6.8%–M1, 5.5%–M2.1), for DM4 (7.6%–M1, 9.6%–M2.1). For (M2.2, M2.3, and M2.4) matrices in the third level, the results were completely consistent since they consist of two elements only. The CR values for the last level showed figures larger than 10% in some cases. Therefore the consistency was improved by applying the procedure proposed for improving the most inconsistent judgments, as explained previously, until achieving CR values less than 10%.

The final results of the weights, after processing the AHP methodology, for the set of criteria, EC, and the priority ranking for options are displayed for each group of DMs in Appendix A (available in the online version of this paper). The results of the priority weights, resulting from the aggregation of the preferences of all groups, are also displayed in Appendix A. Figures 35 illustrate the results graphically.
Figure 3

Total weights of objective class criteria-main criteria (Level 2 of Figure 2) and their rankings.

Figure 3

Total weights of objective class criteria-main criteria (Level 2 of Figure 2) and their rankings.

Figure 4

Total weights of EC (Level 3 of Figure 2) and their rankings.

Figure 4

Total weights of EC (Level 3 of Figure 2) and their rankings.

Figure 5

Total weights of options (Level 4 of Figure 2) and their rankings.

Figure 5

Total weights of options (Level 4 of Figure 2) and their rankings.

The results of the dynamic sensitivity analysis and the stability intervals are shown in Table 5. For example, the economic criterion with a normalized weight of 46.1% has a stability interval of 25.3% with a minimum threshold of 29.6% and maximum threshold of 54.9%. This means the criterion can be weighted within the previous limits without affecting the ranking of options. Details of the dynamic sensitivity analysis are displayed in Appendix B (available in the online version of this paper). Furthermore, details of the performance sensitivity analysis, which shows the priorities of the main criteria, the performance of each option with respect to each main criterion, as well as the overall priorities of the options realized by GMM technique, are displayed in Appendix B, Figure B9 (available in the online version of this paper).

Table 5

Weight sensitivity analysis of group on strategy ranking

Main criteriaMin. weight (%)Value (%)Max. weight (%)Stability interval (%)
Economic 29.6 46.1 54.9 25.3 
Environmental 18.0 24.5 32.2 14.2 
Technical 7.1 10.2 25.5 18.0 
Socio-economic 7.0 19.2 69.2 62.2 
Main criteriaMin. weight (%)Value (%)Max. weight (%)Stability interval (%)
Economic 29.6 46.1 54.9 25.3 
Environmental 18.0 24.5 32.2 14.2 
Technical 7.1 10.2 25.5 18.0 
Socio-economic 7.0 19.2 69.2 62.2 

DISCUSSION

This paper examined the application of MCDA techniques, precisely the AHP methodology, in real-world planning decisions, by considering several stakeholder groups’ preferences and considerations. In our case, the work tackled the reduction of municipal water losses, a critical issue in the water sector realm that challenges municipalities and water purveyors.

By reviewing the preferences of each group of DMs as a separate entity (Figure 3), we noticed that for DM1 the trend was to firstly promote the socio-economic dimensions, with a percentage of 56%, followed by economic aspects, with a value of 26%. This trend is compatible with the mission of this department that focuses on social and economic dimensions in the water sector. The priorities of DM2 at main criteria level were devoted first to the economic issues (66%) followed by the environmental issues (20%). At EC level, their focus was on the water preservation and reduction of waste EC (E5) under the umbrella of the environmental issues, which gathered the majority of this main criterion (90%) as shown in Appendix A, Table A2 (available in the online version of this paper). It reflected their interest in preventing the depletion of the available water resources.

The focus of DM3 was mainly on the environmental dimensions, by assigning a weight of 48% for them, followed by 33% for the socio-economic dimensions (Figure 3). Their trend is in agreement with their profile, which is dedicated to developing and protecting water and environmental resources as well as ensuring that all communities have access to water services in an affordable way.

The leading criteria from the viewpoint of DM4, which represents the concerns of WSSD, were the economic issues, in front with a percentage of 69%, followed distantly by close percentages for technical and environmental issues (Figure 3). Generally, the economic theme comprises the most critical aspect in any strategy to manage water losses at the utility level. Water utilities are in no position to bear an increase in operational and maintenance costs, and cost figures in any proposed solutions to reduce water losses.

At the alternatives level, as shown in Figure 5, the differentiation was clear in the performance of each group of DMs in terms of setting priorities. For DM1, the most preferred option was A9, which comprises employing advanced techniques, while for DM2 and DM4, the most preferred option was A3, which comprises the adoption of strategies to control operational pressure. This option also occupied advanced positions for DM1 and DM3.

Looking at the preferences at the individual level of DMs or at group level after the aggregation of preferences for all groups of DMs, we noticed that options concerned with pressure management (A3), establishing district metered areas (A4), and utilizing advanced techniques (A9) gained high priorities in the ranking of options.

The focus on the pressure management option (A3) is compatible with the strategy of Nablus municipality to restructure and rehabilitate NWDS by following a phased strategy planned to be completed by the year 2025. This strategy comprises the establishment of 27 pressure zones to maintain a range of pressures between 3 and 10 bars, and the pressure in the district zones will be controlled where necessary by using pressure reducing valves (WSSD 2009).

The pivotal motive behind this planned strategy is the insufficient definition of pressure zones associated with the following factors: high differences in the altitudes, which cause water pressure to reach excessive levels, leading ultimately to high physical water losses with more than ten pipe breaks daily, and the intermittent supply, which is associated with overrated pumping facilities that cause over pressure and consequently increase the pipe bursts, and the malfunctioning of water meters (WSSD 2009).

The least preferred options in the overall preferences were, as shown in Figure 5, A8 (control of illegal use of water services) and A10 (public awareness and educational campaigns), which also gained the least attention at individual levels. This result may be explained by the need to secure the customers’ needs for water as a first priority, and later it is possible to switch to these options to improve and develop the process of managing water losses.

The need for further exploration of the implementation of the options requires the incorporation of cost-benefit analysis methods. Consequently, the trade-offs can be considered from the cost-benefit analysis for the different options. The costs, which are quantitative attributes, associated with each option such as capital costs, fixed operating costs and variable operating costs, should be examined against the benefit criteria, which are quantitative and have numerical or qualitative values and depend on the judgments of DMs in order to find the cost effective strategy (Thengane et al. 2014).

CONCLUSIONS AND OUTLOOK

The proposed MCDA method was tested on a real intermittent water supply system by involving leading organizations, policy makers, and affected stakeholders to guide integrated water loss management plans. The conclusions of all groups of DMs related to the evaluation of the decision problem elements and the most appropriate options to implement the strategy were largely consistent. The high complexity of the decision matrices, mainly in the last level of the decision problem, led to CR values beyond the threshold of 10%, but the deviation of results after the improvement of the most inconsistent inputs was negligible, and the options maintained their positions before and after the improving process.

The findings of this study are useful to water utilities in terms of achieving a better understanding and assessment of components of water loss management strategies, their interactions, and dimensions, which are not limited to economic space alone, but are extended to cover environmental, sustainability and potentially health and safety issues. The results reflect the DM's interests in exploring the most effective strategies with a potential to overcome the deficiencies in the water supply system associated with the adoption of the intermittent supply scheme.

To overcome the uncertainty in the input data associated with the uncertainty of DM, due to the lack of information, incomplete knowledge, and the complexity of the decision problem, employing fuzzy set theory as an extension to AHP would be useful in the future.

In the next step, this MCDA model will be upgraded to tackle quantitative data. This will allow the development of a comprehensive framework and could produce a more detailed outcome related to the costs and time plans of the proposed alternatives. This has to be associated with adoption of components of the practical management strategies for reducing water losses to allow, for example, a detailed calculation of water balances for hydraulically separated supply zones.

REFERENCES

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Supplementary data