Predicting Electromagnetic Field Exposure Using Artificial Intelligence Methods

This study explores the application of Artificial Intelligence (AI) in predicting electromagnetic field (EMF) exposure levels in urban environments. Machine Learning (ML) and Deep Learning (DL) models were developed to estimate the electric field (E, in V/m) at specific locations without requiring direct measurements. Measurements were conducted in Thessaloniki, Greece, covering a diverse urban landscape, including commercial and residential areas. The methodology involved collecting EMF data using high-precision equipment, validating and refining publicly available datasets, and extracting key features using Geographic Information System (GIS) tools. Essential input features included distances to the nearest base stations, their installation heights, classification of base stations regarding their emitted power, number of obstructing buildings, built area density, and line-of-sight (LOS) conditions. The AI models demonstrated strong predictive performance, with mean absolute errors (MAE) slightly above 0.3 V/m. The study highlights the importance of proper data preprocessing, feature selection, and integration of real-world measurements into AI-based prediction models. The results suggest that AI can serve as a reliable alternative for EMF exposure assessment, potentially reducing the need for costly and time-consuming field measurements while ensuring compliance with safety regulations.