## 3 Results and Discussions
### Dimensionality Reduction through Important Data Feature Selection Using RF  
A set of trees (100) on the target measured signal data sets was employed to determine the most informative and important subset of features, which were in turn employed as input data to the particle swarm optimization model for predictive path loss analysis. Table 1 and Figure 7 show the acquired signal dataset with constituent features in one of the studied locations and the number of features selected via the methods are displayed. Figure 5: Measured Path loss values versus distance and Prediction comparison with standard COST 231 and ITU-R M.2412-0 model in site 1 Location 3. Figure 6: Measured Path loss values versus distance and Prediction comparison with standard COST 231 and ITU-R M.2412-0 model in site 1 Location 4. Figure 7a reveals the level of importance each measured signal dataset feature attained, and Figure 7b displays each constituent feature (predictor) association estimate. A bigger estimated value indicates a more important constituent feature. As shown in Figure 7a, the RSRP predictor has the highest level of importance with a score of 1. This score is followed by distance with a 0.7 score. Moreover, RSRP, Latitude, and Altitude (m) scored 0.28, 0.3, and 0.4, respectively. On the other hand, BTS Latitude, BTS Longitude, Cell ID, and frequency scored zero each, meaning they have no impact on predictions. Enhanced Prediction Obtained with the Developed Hybrid RF-PS Optimization Method over the Standard Path Loss Optimization Methods  
The results obtained by applying the proposed hybrid RF-PS optimization method over the standard path loss optimization methods are presented in Figures 8-19. In order to benchmark, the ordinary PS and RBF-PS methods used for path loss model optimization are used for performance analysis across the studied locations. Three key performance indicators: RMSE, MAE, and R, are used in this contribution. The proposed RF-PS optimization method showed better prediction performance with lower RMSE values; 3.51-4.33 dB, 2.55-2.98 dB, 3.23-3.54 dB, and 3.11-5.22 dB for locations 1-4, respectively. For standard PS and RBF-PS optimization methods, the RMSEs are quite higher; 3.96-6.08 dB, 2.69-4.33 dB, 4.60-6.38 dB, and 4.160-6.16 dB for locations 1-4, respectively. Overall, the robustness and superiority of the proposed RF-PS method for path loss prediction over the existing techniques are demonstrated. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**BTS LAT.** & \begin{tabular}{c} **BTS** \\ **LONG.** \\ \end{tabular} & **CELL ID** & \begin{tabular}{c} **DIST.** \\ **(m)** \\ \end{tabular} & **RSRP** & \begin{tabular}{c} **RSRP** \\ **LAT.** \\ \end{tabular} & \begin{tabular}{c} **RSRP** \\ **LONG.** \\ \end{tabular} & \begin{tabular}{c} **FREQ.** \\ **(MHz)** \\ \end{tabular} & \
\begin{tabular}{c} **ALT** \\ **(m)** \\ \end{tabular} &
\begin{tabular}{c} **Path Loss** \\ **(dB)** \\ \end{tabular} \\ \hline
6.2497 & 6.2022 & 297 & 100 & \(-\)82.5 & 6.2504 & 6.2028 & 2600 & 170 & 81.3000 \\ \hline
6.2497 & 6.2022 & 297 & 120 & \(-\)68.81 & 6.2506 & 6.2028 & 2600 & 169 & 82.3300 \\ \hline
6.2497 & 6.2022 & 297 & 140 & \(-\)70.19 & 6.2508 & 6.2028 & 2600 & 169 & 97.5000 \\ \hline
6.2497 & 6.2022 & 297 & 160 & \(-\)89.25 & 6.2509 & 6.2028 & 2600 & 169 & 83.8100 \\ \hline
6.2497 & 6.2022 & 297 & 180 & \(-\)75.75 & 6.2512 & 6.2028 & 2600 & 169 & 85.1900 \\ \hline
6.2497 & 6.2022 & 297 & 200 & \(-\)70.44 & 6.2515 & 6.2029 & 2600 & 169 & 104.2500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The acquired signal dataset and the consistent features in one of the studied locations. Figure 7: The constituent feature (predictor) selection of the important estimates and their association using RF. (**a**) Unbiased predictor importance estimates for Agbor L3. (**b**) Predictor association estimates. proposed hybrid approach to cater for highly dimensional measured signal path loss data; hence its superior prediction performances over the standard method.