# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
## 3 Results and Discussion
### Regression Model and Applicability  
The ANOVA results presented in Table 1 indicate that the RSM model developed in this study is statistically significant. The model's high F-value (519.917) and low \(p\)-value confirm that the independent variables have a strong influence on the response variable. The linear terms explain 85.59% of the total variance, with the AT factor contributing 70.14%, followed by the M factor (7.96%). Quadratic terms account for 4.46%, while two-factor interactions contribute 3.48%. Among these, the interaction between M and AT (F = 227.222, 2.91%) emerges as the most significant. The low error rate (6.49%, Adj SS = 505.21) further supports the reliability of the model. In conclusion, the ANOVA analysis indicates that AT and M are the most decisive factors affecting the response variable. The model demonstrates a strong fit in predicting the dependent variable. To enhance the reliability and robustness of the regression model developed in this study, a five-fold cross-validation technique was employed. The dataset was randomly partitioned into five equal subsets. In each iteration, four subsets were used to train the model, while the remaining subset served as the validation set. This process was repeated five times, ensuring that each subset was used for validation once. The average performance metrics--including R2, RMSE (Root Mean Square Error), and MAE (Mean Absolute Error)--were computed across all folds to evaluate model accuracy and generalizability. The use of cross-validation not only mitigated overfitting but also provided a comprehensive assessment of the model's predictive power under varying environmental conditions. The results indicated high consistency across folds, confirming the robustness of the model in estimating surface temperature responses to different vegetation types. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Source** & **DF** & **Contribution** & **Adj SS** & **Adj MS** & **F-Value** & \(p\)**-Value** & **VIF** \\ \hline Model & 14 & 93.51\% & 7278.84 & 519.917 & 95.71 & 0.0000 & \\ Linear & 4 & 85.59\% & 1193.49 & 298.374 & 54.92 & 0.0000 & \\ A (m2) & 1 & 1.69\% & 125.56 & 125.561 & 23.11 & 0.0000 & 1.17 \\ M (\%) & 1 & 7.76\% & 224.17 & 224.170 & 41.27 & 0.0000 & 3.47 \\ WS (m/s) & 1 & 6.01\% & 1.14 & 1.143 & 0.21 & 0.6475 & 4.63 \\ AT (C) & 1 & 70.14\% & 859.27 & 859.268 & 158.17 & 0.0000 & 4.02 \\ Square & 4 & 4.44\% & 250.21 & 62.553 & 11.51 & 0.0000 & \\ A\(\times\)A & 1 & 1.53\% & 118.82 & 118.815 & 21.87 & 0.0000 & 1.00 \\ M\(\times\)M & 1 & 1.25\% & 7.94 & 7.938 & 1.46 & 0.2298 & 7.65 \\ WS\(\times\)WS & 1 & 0.18\% & 65.16 & 65.159 & 11.99 & 0.0008 & 5.41 \\ AT\(\times\)AT & 1 & 1.48\% & 67.85 & 67.854 & 12.49 & 0.0006 & 1.79 \\
2-Way & 6 & 3.48\% & 270.55 & 45.092 & 8.30 & 0.0000 & \\ Interaction & & & & & & & \\ A\(\times\)M & 1 & 0.18\% & 11.93 & 11.928 & 2.20 & 0.1418 & 1.13 \\ A\(\times\)WS & 1 & 0.00\% & 0.00 & 0.001 & 0.00 & 0.9881 & 1.25 \\ A\(\times\)AT & 1 & 0.05\% & 4.11 & 4.115 & 0.76 & 0.3864 & 1.06 \\ M\(\times\)WS & 1 & 0.20\% & 0.85 & 0.850 & 0.16 & 0.6934 & 3.79 \\ M\(\times\)AT & 1 & 2.91\% & 227.22 & 227.223 & 41.83 & 0.0000 & 2.11 \\ WS\(\times\)AT & 1 & 0.13\% & 9.81 & 9.808 & 1.81 & 0.1823 & 4.18 \\ Error & 93 & 6.49\% & 505.21 & 5.432 & & & \\ Total & 107 & 100.00\% & & & & & \\ \hline Model Summary & & & & & & & \\ \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} \\  & & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Analysis of Variance for Leaf Plants. Figure 3, Pareto chart and residual analysis for Leafy Plants. The Pareto chart identifies the standardized effects of main and interaction terms, with AT, M, and the interaction M\(\times\)AT being the most influential. The normal probability plot shows residuals aligning closely along the diagonal, indicating normality.