Introduction and Related Studies

The amount of freshwater suitable for human use is very limited. Available water sources consist of less than one percent of freshwater (Hailu et al. 2019). Water supplied from limited sources is not enough due to the factors such as climate change and increased consumption. Therefore, water must be effectively gained from natural sources to be used as freshwater to be transmitted to the user effectively. Water used in many fields such as agriculture, industry, food, environment, energy, and domestic use is obtained from natural resources. Keeping these resources clean and recycling water are quite complex problems that need to be solved. In this context, water treatment plants play a significant role in the cycle of water recovery and reuse. Water treatment plants include physical, chemical, physicochemical, and biological processes to remove related pollutants. The purpose of this process is to obtain freshwater suitable for reuse. Besides the benefits of water treatment plants, they have adverse impacts on the environment, social life, economy, and natural habitats. In this sense, decision-makers should effectively plan the construction and operation activities of the plants, taking into account the expectations of users of the treated water.

Hakanen and Aittokoski (2010) compare multi-criteria decision-making techniques for water treatment plant design. Boix et al. (2011) use multi-objective optimization techniques to find the most suitable design for multi contaminant industrial water treatment plants. They also use multi-criteria decision-making techniques to evaluate objectives. Kim et al. (2013) prioritize candidate sites for water treatment using a fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). They define the basic criteria for site selection of water treatment plants. Ilangkumaran et al. (2014) use the analytical hierarchy process (AHP) for technology selection for wastewater treatment plants. Nayeb et al. (2014) select the most appropriate wastewater treatment process, considering the climate changes. Hadipour et al. (2016) evaluate alternatives to reuse treated wastewater. AHP technique is used to determine criteria weights. Debnath et al. (2016) propose a decision-making model based on the elimination and choice of expressing reality (ELECTRE) to evaluate cyanobacterial toxins removal methods for water treatment plants. Choudhury et al. (2017) develop an optimization model in order to determine the best technology for water treatment plants. They evaluate twelve alternatives considering six criteria and four sub-criteria. De and Majumder (2017) investigate the quality of a water treatment plant using multi-criteria decision-making methods. Choudhury et al. (2018) identify risk factors for water treatment plants and use The Decision Making Trial and Evaluation Laboratory (DEMATEL) to determine the importance levels of risk factors. Zhang et al. (2019) evaluate the performance of wastewater treatment plants via AHP. Environment, consumption, technical-economic, production management, and equipment status are taken into account as criteria in this study. Skoczko and Oszczapińska (2019) determine the best location for a water treatment plant by AHP. Economic, social, and technical factors are evaluated for four different alternatives. Choudhury et al. (2020) select the ideal site for surface water treatment plants by decision-making methods and the Gini index. Padrón-Páez et al. (2020) use both multi-objective optimization and modified technique for order of preference by similarity to ideal solution (M-TOPSIS) to develop a sustainable wastewater treatment plant design.

Processes and evaluations about water treatment plants are the topics studied in the literature. But there is a limited number of studies on defining and determining weights of public expectations from water treatment plants. Therefore, within the scope of the study, the expectations of the user from the water treatment plants where the water is obtained from natural sources and wastewater are treated. Determination of public expectations from water treatment plants plays a significant role in strategic planning activities of decision-makers. Growing public expectations about especially water quality from water treatment plants increase the pressures on investors and government managers (Meng et al. 2016). People also want plants to lower costs and pay more attention to the environment. Investments in water treatment infrastructure have low financial return expectations, minimizing the possibility of subsidies for investments (McCallum and Viviers 2020). However, governments are primarily responsible for public goods (Rodriguez et al. 2012). In this context, it is crucial to consider public expectations while planning the investments. The pressure on public and private water companies has been increasing in recent years, with stricter environmental regulations and increasing expectations and complaints from people (Stuetz and Frechen 2015; Witherspoon et al. 2012). In the context of growing public concern, managers of water treatment plants need to develop policies considering regulations and expectations (Lebrero et al. 2011). So it is vital to evaluate and prioritize public expectations from water treatment plants.

This study reviews the literature, defines the public expectations from water treatment plants, receives information from experts, analyzes this information, and applies the Trapezoidal Type-2 Fuzzy Analytic Hierarchy Process (T2F-AHP) method to determine the importance weights of expectations. The proposed T2F-AHP methodology is presented in the next section. Public expectations from water treatment plants are given after “The Proposed Trapezoidal Type 2 Fuzzy AHP Methodology” section. “The Real Case Application” section presents the numerical application of the proposed methodology and sensitivity analysis. The conclusions and future direction of this study are provided in last section.

The Proposed Trapezoidal Type 2 Fuzzy AHP Methodology

In this study, a two-level hierarchical model is structured that is suitable to evaluate public expectations from water treatment plants. Then, expert opinions about the main criteria and sub-criteria are collected. These opinions are consolidated, and pairwise comparisons of factors are gained using the Modified Delphi Method. Main and sub-criteria importance weights are obtained by the proposed T2F-AHP method. Then, the results are analyzed and interpreted. The sensitivity analysis is performed to show the reliability of the proposed methodology. Finally, conclusions and future directions are presented. Figure 1 shows the levels of the proposed methodology. The proposed methodology is detailed in the following sub-sections.

Fig. 1
figure 1

The proposed methodology levels

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The Modified Delphi Method

It is necessary to consult more than one expert to gain different perspectives on a particular issue, sometimes. When deciding on a particular issue, it is essential to consider it from different perspectives. By this way, the objectivity of the topic can be tried to be captured. The Modified Delphi method can be helpful in this situation. The method accumulates opinions from experts on a particular topic (Gumus 2009). Then it analyzes the opinions of experts. In Modified Delphi, experts share knowledge, skills, expertise, and opinions until a consensus is achieved (Chang et al. 2008; Hartman 1981; Hsu et al. 2008; Njuangang et al. 2017). The five main steps of the Modified Delphi Method are given in Fig. 2 (Yildiz et al. 2020):

Fig. 2
figure 2

The steps of the Modified Delphi Model

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Step 3 and Step 4 should be repeated to achieve a consensus matrix. The number of experts in decision-making group should be between 5 and 50 (Robbins 1994). Murry and Hammons (1995) also suggest that the method summarizes opinions from a maximum of 30 experts (Chang et al. 2008). Interviews are conducted with anonymous experts (one from the private freshwater sector, one from the public sector, and three academicians) working in Turkey. It is noted that all experts are experienced in water treatment operations and/or related topics.

The Trapezoidal Type 2 Fuzzy AHP Method

People may not be able to clearly define their opinions on some problems. The reason for this is the complexity of their knowledge and thoughts. Decision-making process involves fuzziness and ambiguity (Ayyildiz and Taskin Gumus 2020). The fuzzy logic can be used in such cases. The logic is firstly introduced to the literature by Zadeh (1965). The logic is suitable for both qualitative assessment and subjective judgment in the evaluation processes of decision-making problems. The fuzzy logic focuses on the rationality of uncertainty due to ambiguity. Therefore, fuzzy logic is a superset of traditional logic that has been developed to fit the concept of partial truth, in which accuracy values are between “completely true” and “completely false”. Fuzzy logic can be used to address the qualitative evaluations and subjective opinions in decision-making problems. Qualitative evaluations and subjective opinions include the rationality of uncertainty from ambiguity. The linguistic approach is one of the most used methods to handle uncertainty in judgments and evaluations (Ayyildiz et al. 2020; Wang et al. 2015).

AHP is used for solving multi-criteria decision-making problems. AHP is based on pairwise comparisons of criteria and alternatives. Even if AHP gains information from experts, sometimes it cannot accurately reflect the opinions of experts (Zhang et al. 2018). To eliminate the uncertainty and ambiguity caused by using crisp values in AHP, fuzzy AHP method has been introduced to the literature. In these cases, fuzzy logic can be used with linguistic variables.

These linguistic variables can be defined using different fuzzy sets. Type-1 fuzzy sets are the first developed fuzzy sets. A membership function taking values at a certain point in the interval [0,1] is used in a type-1 fuzzy set. After that, Zadeh (1975) extends the Type-1 fuzzy set and presents the Type-2 fuzzy set. A type-2 fuzzy set is defined by lower and upper membership functions in the interval [0,1], respectively (Ayyildiz et al. 2020). Atanassov (1999) introduces intuitionistic fuzzy (IF) sets. These sets are defined with membership and non-membership functions. Smarandache (1999) presents neutrosophic fuzzy (NF) sets as an extension of IF sets. A truthiness, indeterminacy, and falsity degrees are used to define the NF sets. Hesitant fuzzy (HF) sets can be used to specify multiple degrees of membership (Torra 2010). Also, pythagorean fuzzy sets (Yager 2014) and spherical fuzzy sets (Kutlu Gündoǧdu and Kahraman 2019) are used to define linguistic criteria.

A trapezoidal fuzzy set is used in this study to better represent the membership of elements. This set has also simplicity and tractability (Adams 2013; Bonissone and Decker 1986; Dadone and Vanlandingham 2002; Wu et al. 1988). AHP method can be used with Type-2 fuzzy sets to handle uncertainty and vagueness. Kahraman et al. (2012) and Sari et al. (2012) are the primary studies integrating interval type-2 fuzzy sets with Buckley’s fuzzy AHP (Buckley 1985). Celik and Taskin Gumus (2016) use T2F-AHP to evaluate emergency preparedness of non-governmental humanitarian relief organizations. Celik and Akyuz (2018) employ T2F-AHP and TOPSIS to select ship loader type in maritime transportation. Alegoz and Yapicioglu (2019) integrate fuzzy TOPSIS with T2F-AHP for supplier selection problem. Yilmaz et al. (2019) develop a performance evaluation model for real estate investment trusts based on T2F-AHP and data envelopment analysis. Yılmaz and Kabak (2020) prioritize criteria for humanitarian relief operations via T2F-AHP. Ecer (2020) selects the best supplier for a home appliance manufacturer using T2F-AHP. The procedure of T2F-AHP method is given in Fig. 3 and described below (Celik and Taskin Gumus 2018).

Fig. 3
figure 3

The steps of T2F-AHP

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Step 1: Define the multi-criteria decision-making problem.

Step 2: Construct a pairwise comparison matrix among all the criteria.

Step 3: Examine the consistency of the matrix. Crisp values are used to calculate the consistency ratio Saaty (1977); first, the matrix consistency index (CI) is calculated;

$$CI = \frac{{\lambda _{\mathrm {max}} - n}}{{n - 1}}$$
(2a)

Then CR is calculated;

$$CR = \frac{{CI}}{{RI}}$$
(2b)

λmax is the largest eigenvalue of the pairwise comparison matrix. Random index (RI) depends on matrix order (n). RI is determined via a table developed by Saaty (1977). The consistency ratio should be <0.1.

Step 4: Assign fuzzy values to linguistic terms to create fuzzy pairwise comparison matrix.

$$M = \left[ {\begin{array}{*{20}{c}} 1 & {\widetilde a_{12}} & \cdots & {\widetilde a_{1n}} \\ {\widetilde a_{21}} & 1 & \cdots & {\widetilde a_{2n}} \\ \vdots & \vdots & \ddots & \vdots \\ {\widetilde a_{n1}} & {\widetilde a_{n2}} & \cdots & 1 \end{array}} \right]$$
(2c)

Let \(\widetilde a_{ij}\) be the comparison of criteria i and j.

Step 5: Calculate geometric mean:

$$\widetilde r_i = \left( {\widetilde a_{i1} \otimes \widetilde a_{i2} \otimes ... \otimes \widetilde a_{in}} \right)^{1/n}$$
(2d)

where

$$\root {n} \of {{\widetilde a_{i1}}} = \left( \begin{array}{l}\left( {\root {n} \of {{a_{ij4}^U}},\root {n} \of {{a_{ij3}^U}},\root {n} \of {{a_{ij2}^U}},\root {n} \of {{a_{ij1}^U}};\,H_1\left( {\widetilde a_{ij}^U} \right),H_2\left( {\widetilde a_{ij}^U} \right)} \right),\\ \left( {\root {n} \of {{a_{ij4}^L}},\root {n} \of {{a_{ij3}^L}},\root {n} \of {{a_{ij2}^L}},\root {n} \of {{a_{ij1}^L}};H_1\left( {\widetilde a_{ij}^L} \right),H_2\left( {\widetilde a_{ij}^L} \right)} \right)\end{array} \right)$$
(2e)

Step 6: Determine the fuzzy weights:

$$\widetilde w_i = \widetilde r_i \otimes \left( {\widetilde r_1 \otimes \widetilde r_2 \ldots \otimes \widetilde r_n} \right)^{ - 1}$$
(2f)

Step 7: Defuzzify fuzzy weights to determine the criteria weights:

$$w_i^\prime = \frac{1}{2}\left( {\frac{1}{2}\mathop {\sum}\limits_{i = 1}^4 {\left( {a_i^L + {\mathrm{a}}_{\mathrm{i}}^{\mathrm{U}}} \right)} } \right) \otimes \frac{1}{4}\left( {\mathop {\sum}\limits_{i = 1}^2 {\left( {H_i\left( {A^L} \right) + H_i\left( {A^U} \right)} \right)} } \right)$$
(2g)

Step 8: Normalize the weights:

$$w_i = \frac{{w_i^\prime }}{{\mathop {\sum }\nolimits_{i = 1}^n w_i^\prime }}$$
(2h)

Public Expectations from Water Treatment Plants

In this study, public expectations from the water treatment plants are studied. For the problem, the literature review is performed to search the main criteria, then the suitable criteria for this study are selected by consulting with the experts.

Within the scope of the study, evaluation is made taking into account four main criteria. These criteria are general and difficult to evaluate directly. So some sub-criteria are used to evaluate water treatment plants. The experts are determined by considering their experiences in water treatment operations. These criteria discussed in this study and their brief literature review can be seen in Table 1.

Table 1 Summary of literature review for main and sub-criteria
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Bottero et al. (2011) present a study that handles water treatment technology selection problem based on economics, technological, and environmental aspects. Choudhury et al. (2017) consider the environmental and economic effects of water treatment technology. Akhoundi and Nazif (2018) evaluate technological, environmental, and cultural concerns of tertiary treatment technologies to develop sustainability assessment models for wastewater reuse technologies.

The problem of selecting location or technology for water treatment plants is one of the well-studied topics in the literature. However, there are few studies on defining public expectations and determining weights of expectations from water treatment plants; therefore, this problem is handled in this study. Apart from the other studies, this study presents an extended analysis of this problem. In this context, this study stands out as the most detailed study in which the public expectations in the process are defined and evaluated comprehensively.

In this study, public expectations from water treatment plants are considered and classified into four main criteria as; “Environmental”, “Technical”, “Financial”, and “Social”, through literature review and expert interviews. Each main criterion consists of four different sub-criteria. Environmental laws, which are getting stricter every year, are forcing decision-makers. So environmental concerns must be taken into consideration while making decisions about a water treatment plant. The technical infrastructure of a plant may affect many things, such as water quality, odour density, etc. For this reason, it should be taken into account in corporate strategy decisions. Financial factors are also crucial to the public. Plants are aimed to operate at low cost. Social factors are integrated into the two-level hierarchical criteria structure to make a more comprehensive decision model.

“Environmental” factors are important for water treatment plant strategies. Four different sub-criteria are evaluated in this study under the title of “Environmental” main criterion. “Volume of Waste” is used in the study as it would directly affect the environment and social life. Users care about the amount of waste from the water treatment plant, and want it to be as low as possible. “Noise Pollution” is also important for people who live near water treatment plants. “Land” is directly related to the decision regarding the construction of the water treatment plant. The smaller the area is constructed, the more satisfied users will be obtained. Users also make sure that the plant is compatible with nature. So the “Adaptation to Nature” sub-criterion is taken into consideration.

“Technical” criterion is related to the technical elements of a plant, and this main criterion has four sub-criteria as “Required Technology”, “Infrastructure”, “Personnel”, and “Efficiency/Quality. “Required Technology” refers to the technology need to conduct treatment operations. “Infrastructure” implies to adapt to existing infrastructure. ”Personnel” means that the number of staff to maintain the operations in the plant. “Efficiency/Quality” is related to the desalination process.

By focusing on the “Financial” main criterion, it is seen that all sub-criteria are directly related to the cost of the plant. Four different important components of water treatment plants are considered as sub-criteria. These are “Investment Cost”, “Operating Cost”, “Production Cost”, and “Maintenance Cost”. These four sub-criteria cover basic costs.

The last main criterion is “Social”—and it consists of four sub-criteria, too. “User Reaction” is related to the negative/positive reactions of the people living near the treatment plant. “Job Opportunity” implies the job opportunities that water treatment plants can provide. “Policy” means compliance with government policy. “Impact on Tourism” refers to the positive/negative effects on tourism.

The Real Case Application

After determining the criteria, seven experts working on the water treatment plants related subjects are consulted. Two experts from the Turkish Ministry of Environment and Urbanization, two experts from different metropolitan municipalities and three experts from different water treatment plants, are employed to evaluate the criteria. They are asked to express their opinions about criteria for public expectations from water treatment plants via questionnaire by face to face interviews based on the Modified Delphi method.

The experts use a ten-point scale (Celik et al. 2014), as shown in Table 2, to evaluate the criteria.

Table 2 Fuzzy evaluation scale for the weights
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First, the consolidated pairwise comparisons of the main criteria are evaluated by experts. The pairwise comparison matrix of main criteria created by the experts is shown in Table 3.

Table 3 Main criteria pairwise comparison
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After the main criteria pairwise comparisons are created with the evaluations made by the experts, T2F-AHP steps are run, and the main criteria weights are calculated. However, before this step, it is investigated whether the evaluations of the experts are consistent, and if the pairwise comparisons are not consistent; hence the experts are asked to re-evaluate criteria. If the consistency ratio is calculated as <0.1, the relevant matrix is considered consistent, and the weight calculation step is started. Once the pairwise comparison matrices have been determined consistently, the weights of the criteria are calculated.

As a result of the abovementioned calculations, the weights of the four main criteria are determined using T2F-AHP. Table 4 shows the importance weights of each main criterion.

Table 4 The importance weights of the main criteria
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As can be seen from Table 4, firstly, “Environmental” is determined as the most important main criterion among the four main criteria. According to the table, it can be said that users attach importance to water treatment plants in order to adopt an environmentally friendly approach in water recovery. Except for the criterion of Environmental by the significance level of 0.688, there are no major differences between the other three criteria. As a result of the analysis, the least important main criterion is determined to be the “Finance” with the significance level of 0.086.

Pairwise comparison matrices are created for each of the main criteria and T2F-AHP steps are run, then the local weights of sub-criteria are calculated. The final weights of sub-criteria are calculated using these local weights. Table 5 shows the final weights of main and sub-criteria

Table 5 The importance weights of sub-criteria
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When the sub-criteria are examined, the criterion of “Adaptation to Nature” with an importance weight of 0.443 has the highest importance compared to other sub-criteria. Here, the biggest expectation of the user can be explained as the construction and operation activities of water treatment plants do not adversely affect the natural life. In the second place, “Volume of Waste” sub-criterion comes with a weight of 0.171. For the problem where the main criterion of “Environmental” has the highest importance, it is essential to consider the volume of waste to be generated from the recycled water. For this reason, managers should plan well what the waste volume will be during the activities of the facility.

When focusing on the “Technical” main criterion, the most important sub-criterion is “Efficiency/Quality” with a weight of 0.055; the lowest important sub-criterion is “Personnel” by 0.011 significance level. Focusing on the main criterion of “Financial”, while the “Production Cost” sub-criterion has the highest importance weight with 0.049; the weights of the remaining three sub-criteria are measured to be equal. When looking at the “Social” main criterion, while the “User Reaction” sub-criterion is the most significant sub-criterion with a weight of 0.537; the “Impact on Tourism” sub-criterion is accepted as the criterion of lowest importance with a weight of 0.140. Within the scope of the study, the sub-criteria that weigh the least among sixteen sub-criteria determined in the evaluation of the user expectations from water treatment plants are “Personnel”, “Required Technology”, “Investment Cost”, “Operating Cost”, and “Maintenance Cost”.

Sensitivity Analysis

A sensitivity analysis is conducted to show the reliability and applicability of the proposed methodology. The proposed methodology results are analyzed and discussed by sensitivity analysis. For this purpose, the weights of the main criteria are changed between the two, while the other two are fixed. For example, the weight of the first main criterion is changed by the second, third, and fourth main criterion factors, while the others do not. Then, the T2F-AHP steps are run, and the weights of the sub-criteria are recalculated. The behavior of the proposed methodology is observed in this way. These results help decision-makers to prioritize the criteria and make it easier to analyze the process. As the weights of the main criteria change, so does the final weights of the sub-criteria. Sub-criteria weights according to the changes in main criteria weights are given in Table 6.

Table 6 Sub-criteria weights according to the main criteria weight changes
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According to Table 6, when “Environmental” and “Financial” main criteria are taken into account, the weights of these main criteria are exchanged, then the final weight of “Land” decreases from 0.036 to 0.005, the final weight of “Required Technology” increases from 0.012 to 0.088. “Adaptation to Nature” and “User Reaction” are highly important in most cases. In this context, decision-makers should especially consider- these criteria when deciding on the construction and operation activities of water treatment plants.

Comparison of the Results

In this subsection, the results of the proposed T2F-AHP methodology are compared with AHP. A traditional AHP method is conducted to compare the results. AHP is used in many environment-related areas such as site suitability evaluation (Banai-Kashani 1989; Zelenović Vasiljević et al. 2012), urban landscape management (Srdjevic et al. 2013), drilling fluid evaluation (Sadiq et al. 2003), fishery management (Mardle et al. 2004), urban landscape change evaluation (Zewdie et al. 2018), etc. The steps of AHP are as follows:

Step 1. Construct a pairwise comparison matrix among all criteria with experts. The experts use a scale given in Table 7 (Saaty 1977) to evaluate pairwise comparisons.

Table 7 Index scale for AHP
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Step 2. Examine the consistency of the pairwise comparison matrices. To find the consistency ratio (CR) of a matrix use crisp values proposed by Saaty (1977), first compute the matrix consistency index (CI) using;

$$CI = \frac{{\lambda _{\mathrm {max}} - n}}{{n - 1}}$$
(4a)

Then calculate the CR using;

$$CR = \frac{{CI}}{{RI}}$$
(4b)

Step 3. Normalize the pairwise comparison matrix.

$$n_{ij} = \frac{{a_{ij}}}{{\mathop {\sum }\nolimits_{i = 1}^m a_{ij}}}$$
(4c)

Let aij be the pairwise comparison of parameter i and parameter j, and m represents the number of parameters.

Step 4. Calculate the weights.

$$w_{ij} = \left( {\frac{1}{m}} \right)\mathop {\sum}\nolimits_{i = 1}^m {n_{ij}}$$
(4d)

First, experts’ consolidated pairwise comparison matrix for the main criteria is revised according to the index scale given in Table 7. Then, AHP steps are employed to determine the weight of each main criterion.

The weights of Environmental, Technical, Financial, and Social main criteria are determined. According to AHP results, the main criterion with the highest weight is Environmental with 0.565, and the main criterion with the lowest weight is Financial with 0.055. Weights of the remaining main criteria (Technical and Social) are determined as 0.128 and 0.268, respectively. Thus the main criteria ranking order can be stated as Environmental > Social > Technical > Financial. The ranking order is the same with the result of the proposed methodology. This is important to show the validity of the method.

Conclusions

The amount of freshwater suitable for human use is very limited. Therefore, freshwater must be used effectively. Furthermore, people’s environmental and social awareness is increasing day by day. So, pressure on water treatment plants is increasing day by day, too. Due to the increasing pressure on public and private water treatment plants in recent years, managers of water treatment plants develop investment strategies more carefully. In this context, managers must take into account the public expectations. So, it is vital to evaluate and prioritize public expectations from water treatment plants to develop a more comprehensive strategy.

In this study, determining the importance levels of public expectations from water treatment plants is taken into account. Literature review and interviews with experts from different institutions are conducted, and criteria are determined to evaluate the most important public expectations. A two-level criteria hierarchy is created, and the experts opinions on the criteria are integrated into the study using the modified Delphi method. Then, with T2F-AHP methodology, criteria weights are determined considering the main criteria and sub-criteria.

The contributions of the study to the literature can be defined as follows: (1) T2F-AHP method is adapted to evaluate public expectations from water treatment plants; (2) The most important main and sub-criteria are determined and ranked in order to determine importance levels of each main and sub-criterion; (3) The issues that the decision-makers should pay attention are determined according to the criteria weights considering public expectations; (4) The results of this study can be used as a guide to develop public strategies about water treatment plants; (5) To the best of our knowledge, it is the first study to examine the public expectations from the water treatment plants.

For future studies; different multi-criteria decision-making methods can be included in the methodology. Heuristic methods also can be employed to ensure a more comparative and integrated study. The organizations can be compared by using this work. This approach can be used by public or private organizations.