Green Space Site Selection in Region 15th of Tehran Using Geographic Information System

ality of the green areas distribution. As a result, in order to answer the needs of urban residents in our country and overcome such problems mentioned above the urban managers and decision makers need to apply modern analytical tools. This article attempts to offer a new combinational method in to propose suitability map for distribution of region 15th of Tehran Municipality’s green space to the urban managers and decision makers using FAHP and TOPSIS techniques. Nowadays integrated multi-criteria decision making (MCDM) and geographic information system (GIS) is commonly used in order to solve spatial problems. Different multi-criteria decision making techniques present different methodology with certain limitations and advantages. Our proposed methodology considered 8 green space related criteria to evaluate and prioritise urban green space suitable sites. Proposed methodology has two steps: in step 1, AHP is improved by fuzzy set theory. In this regard, by using fuzzy set theory in AHP method the qualitative judgment can be qualified to make comparison more intuitionistic and reduce or eliminate assessment bias in pairwise comparison process of urban green space criteria. In step 2, obtained results have been used as input weights in TOPSIS algorithm. TOPSIS algorithm by considering ideal and non ideal solution helps decision maker to evaluate ranking locations and select the best one.To deal with vagueness of human thought, Zadeh first introduced the fuzzy set theory, which was oriented to the rationality of uncertainty due to imprecision or vagueness. A major contribution of fuzzy set theory is its capability of representing vague data. The theory also allows mathematical operators and programming to apply to the fuzzy domain. A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function, which assigns to each object a grade of membership ranging between zero and one. Essentially, the uncertainty in the preference judgments give rise to uncertainty in the ranking of alternatives as well as difficulty in determining consistency of preferences. These applications are performed with many different perspectives and proposed methods for fuzzy AHP. In this study, Chang’s (1992) extent analysis on fuzzy AHP is formulated for a selection problem.A triangular fuzzy number (TFN), M, is shown in Fig. 1. A TFN is denoted simply as (m1, m2, m3). The parameters m1, m2 and m3 respectively denote the smallest possible value, the most promising value, and the largest possible value that describe a fuzzy event. The analytic hierarchy process (AHP) is one of the extensively used multi-criteria decision-making methods. One of the main advantages of this method is the relative ease with which it handles multiple criteria. In addition to this, AHP is easier to understand and it can effectively handle both qualitative and quantitative data. The use of AHP does not involve cumbersome mathematics. AHP involves the principles of decomposition, pairwise comparisons, and priority vector generation and synthesis. Though the purpose of AHP is to capture the expert’s knowledge, the conventional AHP still cannot reflect the human thinking style. Therefore, fuzzy AHP, a fuzzy extension of AHP, was developed to solve the hierarchical fuzzy problems. In the fuzzy-AHP procedure, the pairwise comparisons in the judgment matrix are fuzzy numbers that are modified by the designer’s emphasis.An extension of TOPSIS (technique for order performance by similarity to ideal solution), a multi-attribute decision making (MADM) technique, to a set of criteria which are investigated. TOPSIS is a practical and useful technique for ranking and selection of a number of externally determined alternatives through distance measures. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution and the longest geometric distance from the negative ideal solution. It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. In our study, we categorized suitability value of geographic locations for urban green space in region 15th of Tehran Municipality to, very low suitability, low suitability, moderate suitability, high suitability and very high suitability. Comparison of categorized suitability classes in related to final suitability map indicates that the two suitable category (High and very high suitability) of the proposed suitability map together occupy about 20% of study region. AHP is an effective problem solving methodology. Decision problem may contain spatial social, economic, technical and politic factors that need to be evaluated by linguistic variables. Then AHP is one of the most commonly used techniques for such situations. The Criteria set is determined at the beginning in many multi criteria decision making methods and modelled depending upon to these criteria. Multi criteria decision making techniques based on the linguistic evaluations like FAHP helps to make a best selection decision by using a weighting process within the current alternatives via pair wise comparisons. In this methodology by utilizing improved Analytical Hierarchy Process by Fuzzy set theory, weight of each criterionfor urban green space site selection has been calculated. Finally this article introduces an approach that integrates improved AHP with TOPSIS algorithm to support related decisions of urban green space site selection.FAHP is AHP improved by fuzzy set theory which is a useful approach for evaluating complex multiple criteria alternatives involving subjective and uncertain judgment. By using fuzzy set theory in AHP method the qualitative judgment can be qualified to make comparison more intuitionists and reduce or eliminate assessment bias in pair wise comparison process. In further step, TOPSIS algorithm considered ideal and non ideal solution in combination with obtained expert results which have been deducted from FAHP algorithm.