###### Abstract  
The accurate and reliable predictive estimation of signal attenuation loss is of prime importance in radio resource management. During wireless network design and planning, a reliable path loss model is required for optimal predictive estimation of the received signal strength, coverage, quality, and signal interference-to-noise ratio. A set of trees (100) on the target measured data was employed to determine the most informative and important subset of features, which were in turn employed as input data to the Particle Swarm (PS) model for predictive path loss analysis. The proposed Random Forest (RF-PS) based model exhibited optimal precision performance in the real-time prognostic analysis of measured path loss over operational 4G LTE networks in Nigeria. The relative performance of the proposed RF-PS model was compared to the standard PS and hybrid radial basis function-particle swarm optimization (RBF-PS) algorithm for benchmarking. Generally, results indicate that the proposed RF-PS model gave better prediction accuracy than the standard PS and RBF-PS models across the investigated environments. The projected hybrid model would find useful applications in path loss modeling in related wireless propagation environments. p 1 2022  
signal, coverage quality and the signal interference-to-noise ratio [12]. In the existing literature, several generic path loss models for signal coverage and path loss prediction have been proposed [7, 8]. However, these generic models always have precision problems. Thus, without calibrating these models, they can hardly be utilized effectively in practice to produce reliable results, especially in other related environments [13, 14]. The linear least square (LLS) regression [15] method has been engaged to calibrate popular path loss models for enhanced prediction of measured path loss obtained in Irbid, Malaysia, Oman, Nigeria, China, and Baghdad city, among others [13, 14, 16, 17, 18, 19, 20, 21, 22]. Particularly, a recursive algorithm, which is an improved version of the LLS approach was employed to adjust the Okumura-Hata model over an operational CMDA system network [14]. In [16], a statistical LLS-based method was explored to tune the Hata model for path loss prediction in Baghdad City. In [17], a Minimax LLS algorithm was explored for automatic calibration of the Ericsson path loss model. An adaptive LLS method which uses a polynomial function for path loss model adjustment has been presented [18]. A similar method for adjusting the Enceg path loss model was presented [19]. The absolute least square regression procedure was explored to optimize the existing path loss model for robust predictive analysis [23]. The major problem with the LLS path loss model calibration and their extended/modified approaches is sensitivity to outliers on measured signal strength, resulting in large signal prediction errors [24, 25]. A few works have employed a nonlinear least square approach based on different numerical integration schemes to calibrate existing path loss models for optimal prediction [26, 27]. Most recently, the adaptive application of machine learning models, such as neural networks [28, 29, 30], support vector machines [31], genetic algorithms [32], and Gaussian process [33] among others, have gained ground toward efficient path loss predictions. While these machine learning models are good models for prediction analysis, they are, however, computationally demanding and inefficient when engaged for stochastic datasets with high dimensions [34]. In [35], a hybrid structure which combines a ray tracing scheme with random forest (RF) was explored for signal attenuation prediction. The proposed hybrid path loss model showed enhanced prediction compared to the preliminary methods. It is interesting to note that similar hybrid schemes jointly combining RF with Radial Basis Function (RBF) and Neural Network (NN) with RBF have been explored for evapotranspiration and path loss prediction [36, 37].