## 6 Discussion  
The applied MCDMs were analyzed by considering three criteria in negative and positive attributes (presented in Table 9) which are:  
* _Normalization_: Positive evaluation is performed for MCDMs that gives the same results under different normalization techniques, where variations in results are taken as negative. * _MCDM Frequency_: similar ranking results obtained by stand-alone MCDMs are assessed as positive, and high variations are considered as negative. * _AHP-MCDM Frequency_: this criterion is used to investigate the impact of coupling AHP with applied MCDMs, where positive and negative signs show the decrease and increase in frequency variation of final ranking results, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Ranking Frequency of Standalone MCDM**} \\ \hline  & ELE-NS & ELE-NI & MOORA & PROMETHEE & TOPSIS & VIKOR & WPM & WSM \\ \hline ELE-NS & 0 & 2.45 & 4 & 1.41 & 4.47 & 4.69 & 1.41 & 1.41 \\ \hline ELE-NI & 2.45 & 0 & 5.48 & 1.41 & 5.66 & 5.66 & 1.41 & 1.41 \\ \hline MOORA & 4 & 5.48 & 0 & 4.69 & 1.41 & 1.41 & 4.69 & 4.69 \\ \hline PROMETHEE & 1.41 & 1.41 & 4.69 & 0 & 4.90 & 5.10 & 0 & 0 \\ \hline TOPSIS & 4.47 & 5.66 & 1.41 & 4.90 & 0 & 2 & 4.90 & 4.90 \\ \hline VIKOR & 4.69 & 5.66 & 1.41 & 5.10 & 2 & 0 & 5.10 & 5.10 \\ \hline WPM & 1.41 & 1.41 & 4.69 & 0 & 4.90 & 5.10 & 0 & 0 \\ \hline WSM & 1.41 & 1.41 & 4.69 & 0 & 4.90 & 5.10 & 0 & 0 \\ \hline Sum & 19 & 23.48 & 26.37 & 17.5 & 28.24 & 29.06 & 17.5 & 17.5 \\ \hline \hline  & \multicolumn{6}{c}{**Ranking Frequency of AHP-MCDM**} \\ \hline  & ELE-NS & ELE-NI & MOORA & PROMETHEE & TOPSIS & VIKOR & WPM & WSM \\ \hline ELE-NS & 0 & 2.45 & 2.83 & 2.83 & 2.83 & 4.90 & 2.83 & 2.83 \\ \hline ELE-NI & 2.45 & 0 & 1.41 & 1.41 & 1.41 & 3.74 & 1.41 & 1.41 \\ \hline MOORA & 2.83 & 1.41 & 0 & 0 & 0 & 2.83 & 0 & 0 \\ \hline PROMETHEE & 2.83 & 1.41 & 0 & 0 & 0 & 2.83 & 0 & 0 \\ \hline TOPSIS & 2.83 & 1.41 & 0 & 0 & 0 & 2.83 & 0 & 0 \\ \hline VIKOR & 4.90 & 3.74 & 2.83 & 2.83 & 2.83 & 0 & 2.83 & 2.83 \\ \hline WPM & 2.83 & 1.41 & 0 & 0 & 0 & 2.83 & 0 & 0 \\ \hline WSM & 2.83 & 1.41 & 0 & 0 & 0 & 2.83 & 0 & 0 \\ \hline Sum & 21.5 & 13.24 & 7.07 & 7.07 & 7.07 & 22.79 & 7.07 & 7.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Pairwise comparison of frequency error matrix. The frequency error in the ranking results is illustrated in Figure 4, which shows that AHP helps to reduce the irregularity of the final ranking due to the pairwise subjective judgment for calculating criteria weights, which makes more reliable results in decision-making. It is compatible to combine with all the methods except ELECTRE-NS. The increased frequency error in ranking was noticed when the ELECTRE-NS model was coupled with the AHP. It was observed that WSM and PROMETHE were not affected using any normalization techniques and gave the same ranking, which proves that they are the most reliable methods. Moreover, TOPSIS, MOORA and VIKOR models provided improved results after coupling, but the different normalization techniques could affect the final outcome. Based on provided judgments (Table 4), the priority ranking obtained from the majority of the MCDMs showed that planting trees in the urban area was an effective cooling strategy that provides shade, improves air quality, and gives good cooling in certain areas where green walls improve indoor and outdoor air temperature. Additionally, the green walls enhance the aesthetics of the property. The watering method to wet the streets in the summer, planting grass on the surfaces, and water features such as fountains provides the limited cooling extent and requires extra care. While artificial shadings are expensive to install in hotspots, there was no co-benefit associated with this intervention. Figure 4: Graph of calculated error in pairs.