## 3 Results and Discussion
### Proposed Model Against Theoretical Calculation  
It is important to highlight the advantages of our methodology, as described using Equation (1) in Section 2.1, for directly evaluating exposure. To compare the theoretical results obtained through Equation (1) with the monitoring data, we selected two types of sites: LOS (line of sight) and NLOS (non-line of sight). The theoretical calculations yield significantly higher values because the nominal power is used in place of the actual antenna transmitting power in the calculations based on Equation (1). Our model incorporates four terminal devices as input parameters, and to optimize the prediction, the data transmission mode is selected for the terminal service mode. Table 8 presents the comparison results, with Sites 1 and 2 being LOS sites and Sites 3 and 4 being NLOS sites.  
We review the results obtained earlier. Equation (1) is expected to provide a reasonably accurate estimation of the electromagnetic (EM) field levels in LOS conditions. Since the  
Figure 11: The connection between the maximum decision tree depth and the RMSE of ten-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**E-Field Intensity (V/m)**} & \multicolumn{3}{c}{**MAPE (\%)**} \\ \hline
**Index of Site** & **Measurement** & **Equation (1)** & **Proposed Model** & **Equation (1)** & **Proposed Model** \\ \hline
1 & 2.31 & 13.21 & 2.51 & 473 & \\ \hline
2 & 1.75 & 8.81 & 1.89 & 431 & \\ \hline
3 & 1.48 & 11.41 & 1.53 & 725 & \\ \hline
4 & 2.17 & 21.17 & 2.35 & 923 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Proposed model versus theoretical computation.  
nominal power of the base station is higher than its actual transmission power, which varies depending on the terminal's different statuses, the computed value from Equation (1) is considerably higher than the measured value. To address variations in transmission power, our model incorporates the number of terminals and terminal services as input parameters. By accounting for these factors, the proposed approach bridges the gap between theoretical calculations and real-world measurements, offering more accurate and realistic estimations of EM field levels.  
Equation (1) does not incorporate the presence of multiple propagation channels, such as direct, reflected, and diffracted paths, between the base station and the test point in our scenarios, and it only considers the direct path in NLOS situations. As a result, Equation (1) does not provide sufficient information to accurately assess the method's performance in NLOS locations, especially in dense environments with complex propagation channels, where performance is likely to be significantly affected. Consequently, the error in Equation (1) is larger in NLOS scenarios. In contrast, the proposed model accounts for environmental complexity, allowing it to incorporate the multipath effect. By integrating these factors, our model provides more accurate and reliable estimations of EM field levels in NLOS conditions. The inclusion of multipath effects and environmental factors in our model mitigates the limitations of Equation (1), leading to improved performance in estimating EM field levels in NLOS sites.