Multi Criteria Analysis for decision support in the field of MAR

Multi Criteria Analysis (MCA) is the composition of techniques that are potentially capable of improving the transparency, auditability, and analytical rigour of the possible decision and may be applied in many different fields of science and technology (Dunning et al., 2000; Romeo and Rehman, 1987). Originally, MCA was developed to select the best alternative from a set of competing options by analysing the selected criteria that presents the options best. MCA evolved as a tool for decision making in the 1960s and 1970s (Hajkowicz, 2007). Over the years, MCA has received attention by a diverse range of disciplines and has evolved into a wide range of decision aiding techniques (e.g., Munda, 1995). The application of MCA can be for ranking of alternatives, product evaluation, formative evaluation, improvement of negotiation, combined product and process evaluation, structuring of the decision problem and assessment of the overall impacts (Sharifi, 2003). Nowadays, MCA is an established methodology in the professional and scientific community. Overtime, MCA has received particular attention in water resources management. In the field of MAR project planning and management, the application of MAR is scant (Rahman et al., 2010).

Spatial Multicriteria Analysis (SMCA) is another application of MCA, where a number of thematic maps are considered as the criteria and the analysis considers the spatial distribution of the alternatives. The main steps of SMCA are basically similar to traditional MCA analysis so in this report, the general description and the workflow of both (traditional and spatial) MCA are discussed under the same heading.

Many factors need to be considered during the site selection process for MAR projects. Complex regional characteristics, heterogeneities in surface and/or subsurface characteristics, and variable groundwater qualities make site selection for MAR difficult (Anbazhagan et al., 2005). Apart from these hydrogeological considerations, other factors such as political and social factors are important in the decision-making process. National and international water policies, natural conservation regulations, environmental impact assessments, and socio-economic considerations make the site selection procedure complex. Complexity increases when MAR project managers are from different disciplinary backgrounds; this may often lead to disagreements concerning which criteria to give more weight to in the decision-making process. These conflicts always need to be dealt with before the MAR project is implemented. GIS and the traditional DSS alone do not effectively facilitate the implementation of MAR project parameters, which are equally based on complex decision criteria and spatial information (Jun, 2000). GIS based analysis methods are poor in dealing with uncertainty, risks, and potential conflicts; therefore, there is a large possibility of losing important information, which in turn may lead to a poor decision (Bailey et. al., 2003). Multi-criteria Decision Analysis (MCDA) integrated into GIS (SMCDA) provides adequate solution procedures to this problem because the analysis of potential MAR projects may be done more comprehensively and at a lower cost. Variable project sites, risks, MAR techniques, policies, and limits in geological as well as social, environmental, and political realms can easily be considered by the SMCDA approach (Calijuri et al., 2004).

MCDA is helpful in identifying priorities for a given MAR project (Gomes and Lins, 2002). The integration of MCDA techniques with GIS has considerably advanced the traditional map overlay approaches for site suitability analysis (e.g., Malczewski, 1996a; Eastman, 1997). MCDA procedures utilize geographical data, consider the user‘s preferences, manipulate data, and set preferences according to specified decision rules (Malczewski, 2004). The advantage of integrating GIS with MCDA has been elaborated by many authors (e.g., Malczewski, 1996b; Jun, 2000; Gomes and Lins, 2002; Sharifi and Retsios, 2004). According to Malczewski (2004), the two critical considerations for SMCDA are: (i) the GIS capabilities of data acquisition, storage, retrieval, manipulation, and analysis;

and (ii) the MCDA capabilities for combining the geographical data and the manager‘s preference into unidimensional values of alternative decisions. A number of methodologies have already been developed for SMCDA in different fields of science and engineering to select the best alternatives

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from a set of competing options (e.g., Sharifi et al., 2006; Zucca et al., 2007).

A brief description of the different steps of both the spatial and non-spatial MCA analysis is given below:

(a) Problem analysis

The first step of starting an MCA procedure is to identify the problem and to analyse it properly because the choice of MCA methods and the steps are also dependent on the problems.

(b) Choice of criteria and subcriteria

Criteria, which give an indication of the appropriateness of the alternatives to achieve the objective, are used to evaluate the objectives of a decision problem. Therefore, the selected criteria should represent accurately the objective of the problem. Sharifi, 2003 stated that the selected criteria must be SMART (Specific, Measurable, Attributable, Realistic, and Time bound). CIFOR, (1999) proposed nine attributes for the selection of criteria, which are (1) Relevance (2) Unambiguously related to the assessment goal (3) Precisely defined (4) Diagnostically specific (5) Easy to detect, record and interpret (6) Reliability (7) Sensitive and responsive to the changes in the system (8) Provides a summary or integrative measure over space and time, and (9) Usefulness to users. Therefore, a critical review of the system in the particular field should be performed to select criteria for an MCA analysis.

(c) Hierarchy of criteria and subcriteria

If the number of selected criteria is very large, it is reasonable to subdivide the criteria into groups and/or subgroups. It is advisable to make a three-level hierarchy ( cited in Pfeffer, 2002). They found that a hierarchy more than three levels would not increase the insight of the problem. The criteria can be bottom-up or top-down. These two categories can be mixed up by using a computer program.

Based on the hierarchical structure concept, Analytical Hierarchy Process (AHP, Saaty 1980) has been developed.

(d) Standardization of Criteria/Subcriteria

The criteria or subcriteria can be measured in different measurement units. For example, slope is measured in percent (%), aquifer thickness is measured in meters, etc. To make all the criteria comparable they have to be standardized (Sharifi and Retsios, 2004). Valuation of the best possible value to 1 or 100 and the worst possible value to 0 will satisfy the goal. A number of techniques are available for standardization, such as the linear scale transformation (Voogd, 1983), the mid-value method (Bodily, 1995), the Evalue method (Beinat, 1997), the convex value function and concave value function (Keeney, 1992), the utility function approach (Eriksen and Keller, 1993), etc.

Malczewski, 2000 stated that common practice is to use the standardized score range procedure, which is associated with a non-linearity problem. The author recommended to use value function

analysis techniques for standardization. There are several value function analysis techniques, such as the mid value method, the Evalue method, the convex value function and the concave value function etc. Only the value function technique was used in the study.

(e) Relative weight of criteria and subcriteria

The relative importance of the criteria can be achieved by assigning a weight to the criteria. So a relative weight can be defined as a value assigned to an evaluation criterion that indicates its importance relative to other criteria under consideration (Malczewski, 1999). The weights are usually normalized to sum to one. There are a few methods to assign weights to the criteria, such as direct weighting (Hämäläinen and Pöyhönen, 1997), ranking method (Wilcoxon, 1945), rating method ( Webster, 2008), pair-wise comparison method (Saaty, 1980), trade-off analysis method (Keeney and Raiffa, 1976), etc. The methods differ in several important ways. Malczewski, 1999 stated that the ranking or rating method is applicable if ease-of-use, time, and cost are involved in generating of weights. On the other hand, pair-wise comparison or the trade-off analysis method is suitable if accuracy and theoretical foundations are major concerns. Empirical applications suggest that the pair-wise comparison method is one of the most effective techniques for spatial decision making including GIS base approaches (Eastman et al., 1993, Malczewski, 1996, Malczewski, 1999). In this present study, the direct weighting method (Hämäläinen and Pöyhönen, 1997) and pair-wise comparison (see section 2.2.3 for description) method are used.

(e) Combination of criteria and subcriteria

The overlay MCDA plays an important role in many GIS applications. Boolean logic and Weighted Linear Combination (WLC) are the most popular decision rules in GIS (e.g., Eastman, 1997;

Malczewski and Rinner, 2005) and both can be generalized within the scope of Ordered Weighted Averaging (OWA) (e.g., Malczewski and Rinner, 2005; Malczewski, 2006). In OWA, a number of decision strategy maps can be generated by changing the ordered weights. Several OWA applications have been implemented already (e.g. Rinner and Malczewski, 2002; Calijuri et. al., 2004; Malczewski et al., 2003; Malczewski, 2006). The Analytical Hierarchy Process (AHP), proposed by Saaty (1980), is another well-known procedure. This procedure is important for spatial decision problems with a large number of criteria (Eastman et al., 1993). AHP can be used to combine the priorities for all levels of a ―criteria tree,‖ including the level representing criteria. In this case, a relatively small number of criteria can be evaluated (Jankowski and Richard, 1994; Boroushaki and Malczewski, 2008). The combination of AHP with WLC and/or OWA can provide a more effective and robust MCDA tool for spatial decision problems. Boroushaki and Malczewski (2008) implemented AHP-OWA operators using fuzzy linguistic quantifiers in the GIS environment, which has been proven to be effective.

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