![]() ![]() However, the predefined interval boundaries in the FAHP further increase the subjectivity that prevails in group decision making. While the FAHP model treats uncertainties with fuzzy sets having fixed boundaries and depends on the type of membership function, in RNs, the interval boundaries are flexible and adapted to the imprecisions that govern the data. Different interval sizes are the result of different mechanisms for treating uncertainty and imprecision ( Beer, Ferson, & Kreinovich, 2013). In contrast, FAHP and IR’AHP represent coefficient weight using interval numbers with different interval sizes. Thereby, it does not take into account the uncertainties that exist in the group decision-making process ( Li et al., 2016). The traditional crisp AHP method calculates weight coefficients using crisp numbers. Hamid Reza Pourghasemi, in Spatial Modeling in GIS and R for Earth and Environmental Sciences, 2019 15.5 Discussion High-rise building (medium concrete roof) Prefabricated buildings with natural hewn stone Afterward, for mapping the UHI modeling, the study adopted Eq. The results of class rating, factors rating, and CR values of all thematic data layers are presented in Table 3.4. On the other hand, population distribution (0.044) and LULC (0.022) received lower values in scale importance as compared with other factors. The weight ratings of building material, building roof type, and building roof reflectance are 0.200, 0.178, and 0.156, respectively, and they were found to be important conditioning factors in UHI modeling. The computed CR implies a reasonable level of consistency (<0.1), which is enough to recognize the factors rating in UHI zonation. The rating values of each conditioning factors and corresponding CR values are shown in Table 3.4. This rating was computed through Expert Choice software, which reveals that the rating of each factor was prioritized subject to cognitive limitation with uncertainty and users subjectivity ( Pourghasemi et al., 2012). The pairwise comparison of an element at each level of the hierarchy allows the assessment of the relative significance of all elements in the hierarchy and to assign a rating to conditioning factors to a level as opposed to a specific element in the same level ( Saaty, 1980). The second level involves contributing factors, and the third level considers subclasses pertaining to each of the factors in the second level. The top level of the hierarchy defines a general objective of the problems, i.e., quantification of UHI. In this multicriteria decision-making situation, AHP model is the most suited technique where the contributing factors can be organized in a hierarchical structure ( Saaty, 1980). Apart from that, reciprocal pairwise comparison matrix is established to perform AHP in which each thematic factor based on 9-point rating scale ( Table 3.3) is employed ( Saaty, 1977). Subsequently, the conditioning factors are synthesized and rated to determine the priorities to be assigned according to their importance ( Saaty & Vargas, 2012 Pourghasemi et al., 2012 Shahabi & Hashim, 2015). In the AHP model, the conditioning factors are arranged in a hierarchical order and assigned numerical values on the basis of relative importance of factors by user subjectivity ( Saaty, 1980). It is a multicriteria decision-making multiobjective approach that allows users to infer a rational agreement on scale of preference ( Saaty, 1980). The AHP is a semiquantitative and flexible tool, which involves a matrix-based pairwise comparison of the contribution of different conditioning factors and analyzes complicated problems focusing on site selection, regional planning, routing modeling, and modeling of environmental phenomena ( Pourghasemi, Pradhan, & Gokceoglu, 2012 Shahabi & Hashim, 2015). ![]()
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