## 4 Conclusions  
The extensive evaluation of 410 machine learning models, which includes k-nearest neighbors (kNN), neural networks, and decision trees, established that the kNN algorithm is the most effective in predicting the strength of the electromagnetic field (EMF) in urban settings, especially when using geographic features as predictive variables. The kNN model consistently delivered the most reliable performance, achieving a root mean square error (RMSE) of 1.63 with a standard deviation of 0.20, thus surpassing both neural networks and decision trees in terms of predictive accuracy and stability. The superior performance of the kNN model can be attributed to its ability to exploit a comprehensive dataset that integrates not only EMF measurements but also various geographical and urban characteristics, including population density, levels of urbanization, and specific building attributes. To gain a deeper understanding of the model's predictive behavior, SHAP (SHapley Additive exPlanations) was employed as a key explainable machine learning technique. This approach provided valuable information on the factors driving the model predictions, revealing that the most influential characteristic was the total volume of buildings in the vicinity of each antenna. This characteristic consistently exhibited a significant impact on all predictions, underscoring its critical role in the decision-making process of the model. Population density emerged as another important factor, although its influence was less pronounced than that of building volume. Other features, such as the total surface area of buildings, contributed moderately to the predictions, whereas geographic coordinates like longitude and latitude were found to have minimal impact on the model's outcomes. Our research significantly advances the field of environmental monitoring by constructing highly accurate and dynamic maps of electromagnetic field strength using state-of-the-art machine learning techniques. The integration of a comprehensive dataset and explainable AI methods has enabled the identification of key factors that affect the strength of the EMF, leading to the development of robust and reliable predictive models. These EMF maps provide valuable information for policymakers, urban planners, and researchers, facilitating data-driven decision-making regarding public health, infrastructure development, and urban management. The findings of this study demonstrate the transformative potential of machine learning in the handling of complex environmental challenges, paving the way for more informed approaches to monitoring and managing electromagnetic exposure in urban environments. The creation of electromagnetic radiation level maps will contribute to various fields, including the following: (a) Risk assessment: These maps will facilitate the evaluation of environmental risks by identifying areas with elevated electromagnetic radiation levels. This information is essential for formulating risk management strategies and implementing preventive measures to protect public health and the environment. (b) Developing best practices: The maps will provide valuable data and insights that can be used to evaluate and optimize radiation protection protocols for the general population exposed to electromagnetic radiation. A subsequent step in the investigation involves not only broadening the comparative analysis to a wider range of cities but also improving the dataset with additional geographic variables. A more ambitious aim would be to incorporate temporal parameters to facilitate the development of real-time maps. Conceptualization, Y.K. ; methodology, Y.K. and K.V. ; software, I.P., I.K. and S.Z. ; validation, Y.K. and I.P. ; formal analysis, Y.K. and I.T. ; investigation, Y.K., K.V. and I.T. ; data curation, Y.K., I.P., K.V., S.Z. and I.K. ; writing--original draft preparation, Y.K. and I.P. ; writing--review and editing, Y.K., T.P., and I.T. ; visualization, I.P. ; supervision, Y.K., T.P. and I.F. ; project administration, Y.K., T.P. and I.F.