## 2 Methodology
### Dataset  
We adopt a two-pronged approach to feature selection, considering both the actual measured values and the principles of electromagnetic radiation to avoid dimensionality issues and overfitting caused by irrelevant features. We carefully select the parameters to be used as inputs and outputs for the random forest regression model, as listed in Table 2. The measurement campaign is conducted on both weekends and weekdays. The training, validation, and prediction processes of the random forest model are illustrated in Figure 6. The random forest algorithm was developed using Python 3.12.0, with implementation carried out through the scikit-learn library. During the training phase, the dataset includes inputs such as the antenna's transmission power, antenna gain, terminal service modes, the number of 5G terminals, the distance between 5G terminals and the base station, the environmental complexity around the 5G base station, and the monitored electric field intensity. This dataset is used to train the pre-trained random forest model, resulting in a well-trained model. In the validation phase, the same set of inputs--antenna transmit power, antenna gain, terminal service modes, number of 5G terminals, distance from 5G terminals to the base station, environmental complexity, and monitored electric field intensity--are used. The output of the model is the predicted electric field intensity, and by comparing the predicted and monitored values, the error's MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Squared Error) are calculated. In the prediction process, the dataset (new data) includes inputs such as the antenna's transmit power, antenna gain, terminal service modes, the number of 5G terminals, the distance between the terminals and the base station, and the environmental complexity surrounding the 5G base station. The trained random forest model receives these input parameters and automatically predicts the electric field's intensity. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Feature** \\ \hline \(G\) & Antenna gain & Input \\ \hline \(R\) & Distance between base station and terminal & Input \\ \hline \(P\) & Antenna transmit power & Input \\ \hline \(C\) & The intricacy of the surrounding environmental situations for the base station & Input \\ \hline \(N\) & Number of terminals & Input \\ \hline \(TSM\) & Terminal service mode & Input \\ \hline \(E\) & Average electric field intensity over 6 min & Output \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The random forest regression model’s input and output. Figure 6: Random forest model flow diagram. Selecting the right features is the first step in accurately estimating electromagnetic radiation levels. As shown in Equation (1), factors such as antenna transmit power (P), antenna gain (G), and the distance (D) from the 5G base station significantly influence the electric field radiation level. In practice, three major Nigerian network operators--MTN, Globalcom, and Airtel--use a specific type of transmitting antenna to service the terminal. Table 3 provides details on the nominal values of antenna transmit power for each operator. While there may be slight variations between these nominal values and the actual transmit power in certain monitoring conditions, this difference does not impact the accuracy of our predictions. The gain (absolute gain) is represented as a dimensionless ratio of the power output in a specific direction to the power radiated by the reference antenna. The model's ability to predict the strength of the electromagnetic radiation field is not solely reliant on these variables; instead, it performs a comprehensive analysis using multi-dimensional data. As illustrated in Figure 2, the monitoring point is equipped with a terminal, and the antenna beam is specifically designed to serve this terminal. Therefore, the gain referred to here is the nominal gain (P) of the antenna.