## 7 Comparison with Other Multi-Criteria Decision-Making Techniques
### Comparative Results  
The comparison of the TOPSIS approach with the aforementioned techniques was done in the context of the consolidated results presented in Tables 3 and 4 for [PERSON] and [PERSON]. The objective of the analysis was to investigate whether the trends obtained using the TOPSIS approach match with the trends observed using the combined effect of all MCDM approaches. For this purpose, an approach similar to the ranking approach proposed by [PERSON] et al. [43] was adopted in this study and works as follows. Each turbine in Tables 3 and 4 was evaluated with regard to the three methods. Then, based on the outcome of each method, scores were assigned to each turbine. Note that there were 15 turbines. Therefore, the best turbine was given maximum 15 points which was the highest score. Then, the second best turbine was given 14 points and so on. The worst turbine scored 1 point. In order to understand the approach, consider the results in Table 5. For TOPSIS, the value of \(R_{i}\) for each turbine is given. Next to this value, the score is given with respect to the standing of the turbine. The same approach was used for the other methods, as shown in the table. The second to last column of the table shows the total score obtained by adding the individual scores from each method. The higher the total score, the higher the ranking. The same approach was followed for the turbines for Wejh, as shown in Table 6.  
In order to be consistent, the limits and the weights used for different methods were the same. More specifically, the TOPSIS approach described in Section 5 used the positive ideal values and negative ideal values extracted from the input data for all turbines. The same input data were used to define the upper limits in weighted sum method, the upper and lower limits in fuzzy logic, and the ideal values in goal programming. Furthermore, since five decision criteria were considered, equal weights (i.e., 0.2) were assumed for each criterion in TOPSIS and WSM. For FL and GP, the structure of the decision function automatically assumes equal preference for each objective, as is obvious from Equations (9) and (10). It is obvious from Tables 5 and 6 that different MCDM methods produced different results. This trend is somewhat common in comparative studies in the domain of MCDM [43; 44]. However, as stated in Section 1, the objective of this comparison was to investigate the trends of the two sites when TOPSIS was used versus the trends when the combined effect of all MCDM methods was analyzed. The reason for studying the combined effect was inspired by the study of [PERSON] et al. [43], who those the best alternative by aggregating the rankings obtained using different methods. In order to address the aforementioned objective, the combined scores in Tables 5 and 6 were scaled to a level of 1-15. This was done to be consistent with the 1-15 score level for the TOPSIS approach. The last columns of Tables 5 and 6 show the scaled scores for Turaif and Wejh respectively. In order to further elaborate the comparative trends, Figures 7 and 8 show the scores for each turbine for the two sites using the TOPSIS and combined MCDM approaches, respectively. As observed in Figure 7, six turbine types show higher ranks for Turaif when compared with their ranks with Wejh, while five turbine types show lower ranks for Turaif. For four turbine types, both Turaif and Wejh show equal rankings. Note that the notion of ranking is different from scores here. Ranking is simply reflecting the relative superiority of the same turbine type when used for the two sites, irrespective of the scores the turbine obtained for the two sites. In the same fashion, the relative ranking for all turbine types is plotted in Figure 8, where it can be observed that six turbine types had better ranks for Turaif, and for the same number had lower ranks for Turaif; three turbine types had equal ranks.