## 2 Methodology
### Estimation of Frequency-Selective Signal Field Strength  
Lekki is a narrow stretch of land situated between two major bodies of water: the Atlantic Ocean to the south, known for its sandy beaches, and the Lekki and Lagos Lagoons to the north, which form its natural northern boundary. This positioning gives Lekki a long, strip-like shape, extending roughly from the east to the west. The urban development in Lekki follows a linear pattern centered around the Lekki-Epe Expressway, which acts as the main transportation route. The region is divided into phases (like Lekki Phase 1 and 2) and various residential states, resulting in a grid-based street network radiating from the main road. The topography is mostly flat and low-lying and only a few meters above sea level, making it prone to flooding during heavy rainfall or tidal surges. The landscape includes mangroves, swamps, and reclaimed land, with areas like the Lekki Conservation Centre helping to protect the region's biodiversity and wetlands. Lagos Island is a compact, naturally formed island with an irregular coastline, and it is surrounded by Lagos Lagoon, Five Cowrie Creek, and other waterways. It has high-density urban structures, blending residential, commercial, and historical areas. The road layout combines planned grid patterns and organic street designs, especially in older neighborhoods. Like much of Lagos, the island is flat and close to sea level. It is well-connected to surrounding districts via key bridges such as the Carter Bridge, Eko Bridge, and Third Mainland Bridge, reflecting a mix of natural geography and intensive urban development. Ajah is shaped by its coastal, linear layout, running along the Lekki-Epe Expressway, with growth patterns that reflect both planned estates and informal expansion. The area is flat and low-lying and bordered by creeks and Lagoons, which contribute to its partially fragmented, organically growing form. Ajah's development is influenced by nearby wetlands and water bodies, resulting in a mix of residential sprawl and natural features. At the millimeter-wave range, propagation is highly sensitive to material properties around the base station, with strong attenuation through the walls, glass, and trees; it behaves almost like line-of-sight propagation. Common materials found around the study areas are dry concrete, brick, glass (window), dry and wet asphalt, vegetation, metallic poles ocean (salt water), and fresh water. Based on the defined inputs and outputs, the dataset for training and evaluating the random forest regression model is derived from the measured results of 52 base stations. The dataset consists of 1344 measured data points, which are randomly split into two parts: 942 data points (70%) for the training set and 402 data points (30%) for the test set. The random forest regression model is trained using the training set, and the optimal parameters are selected. The performance of the model is assessed using the MAPE and RMSE on the test set as  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|y_{i}^{\prime}-y_{i}\right|}{y_{i}}, \tag{8}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}^{\prime}-y_{i}\right)^{2}}, \tag{9}\]where \(y_{i}\) is the measured electric field strength in the test set, \(y^{\prime}_{i}\) is the estimated electric field strength, and \(n\) is the number of data points in the test set. The maximum depth of decision trees (\(D\)), the number of decision trees (\(\mathbb{N}_{1}\)), and the maximum number of features that can be employed in the creation of decision trees (\(\mathbb{N}_{2}\)) are the three key parameters of the random forest regression model. The maximum depth of decision trees is used to limit the depth of decision trees when the model with many features is trained with a small amount of data. This helps prevent the model from becoming overly complex and from underperforming in terms of generalization. The complexity and ability of the model to capture relationships are primarily influenced by the number of decision trees.