## 5 Conclusions  
A path loss model with a good prediction accuracy is required for the determination of wireless network parameters such as transmitting power, cell radius, antenna height, number of cell sites, and their optimal position. The attainment of the required data rate is dependent on the proper setting of the parameters such that there is good signal coverage and no interference. The dependency of path loss on the environment led to the inclusion of satellite images of network location in developing machine learning path loss models such that the influence of the environment is learned. A path loss predictive model that handles multiple frequencies, multiple antenna heights, and multiple environments were developed in this work. The model takes in a combination of numeric features and features extracted from satellite images to make the path loss prediction. XGBoost was the algorithm used in model development and was selected based on its good performance in several applications. Several hand-crafted feature extraction methods, their combinations, or combinations of hand-crafted features and CNN features, were tested. The hand-crafted feature extraction methods included LBP, HOG, GLCM, DWT, and SFTA. None of the hand-crafted features improved accuracy compared with using numeric features alone; instead, the accuracy was reduced. Combining GLCM with any of the hand-crafted feature extraction methods decreased performance below that of GLCM features. When CNN features were combined with any of the hand-crafted feature extraction methods, combinations with LBP or GLCM resulted in accuracies better than that of CNN features. A combination of CNN and GLCM features from the images had the best performance overall. This resulted in a 9.4272% improvement in RMSE for combined environments over when no satellite image features were used in model development. A comparison was performed to evaluate the performance of other ML algorithms on the combination of numeric, GLCM, and CNN features. Random Forest, Extreme Learning Trees, Gradient Boosting, and K Nearest Neighbor were used for this purpose. XGBoost had the best performance metrics and also has an advantage of a fast training time over them. Features extracted using CNN were observed to have the highest impact on the model's output based on SHAP. Although the model's accuracy is good, there is a need for more training data from different environments and network parameters such that a single model can be developed and used for pre-network deployment path loss prediction. Meanwhile, the study demonstrated the possibility of developing such a model because an RMSE value of  
Figure 12: Dependence plot of the feature “_t_2” in the model developed with a combination of non-image features and CNN + GLCM features. Feature “_t_2” has the highest interaction index with the GLCM feature “correlation”.  
3.6682 dB was obtained when combined data were used. This RMSE value is acceptable for a path loss model as it is below 7 dB. In our future work, the model's performance for the different sub-datasets will be explored, and methods for improving the accuracy of sub-datasets with low accuracy will be addressed.  
Conceptualization, U.S.S., D.T.C.L. and O.A.M.; methodology, U.S.S., D.T.C.L. and O.A.M.; formal analysis, U.S.S.; data curation, U.S.S.; writing--original draft preparation, U.S.S.; writing--review and editing, U.S.S., D.T.C.L. and O.A.M.; supervision, D.T.C.L. and O.A.M. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Universiti Brunei Darussalam, grant number RSCH/1.18/FICBF(a)/2022/004.  
Not applicable.  
The data used in this research are available on the github repository, \"A Multiple Environment and Multiple Network Parameter Path Loss Dataset\".  
We express our sincere gratitude to Universiti Brunei Darussalam for the provision of resources used in this research.  
The authors declare no conflict of interest.