TY - JOUR
T1 - A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities
AU - Kiouvrekis, Yiannis
AU - Givisis, Ioannis
AU - Panagiotakopoulos, Theodor
AU - Tsilikas, Ioannis
AU - Ploussi, Agapi
AU - Spyratou, Ellas
AU - Efstathopoulos, Efstathios P.
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2025/1
Y1 - 2025/1
N2 - The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, enhancing the field of environmental monitoring with the aid of sensor-based data collection. Our machine learning models consist of a novel and comprehensive dataset collected from a network of strategically placed sensors, capturing not only electromagnetic field readings but also additional urban features, including population density, levels of urbanization, and specific building characteristics. This sensor-driven approach, coupled with explainable AI, enables us to identify key factors influencing electromagnetic exposure more accurately. The integration of IoT sensor data with machine learning opens the potential for creating highly detailed and dynamic electromagnetic pollution maps. These maps are not merely static snapshots; they offer researchers the ability to track trends over time, assess the effectiveness of mitigation efforts, and gain a deeper understanding of electromagnetic field distribution in urban environments. Through the extensive dataset, our models can yield highly accurate and dynamic electric field strength maps. For this study, we performed a comprehensive analysis involving 566 machine learning models across eight French cities: Lyon, Saint-Étienne, Clermont-Ferrand, Dijon, Nantes, Rouen, Lille, and Paris. The analysis incorporated six core approaches: k-Nearest Neighbors, XGBoost, Random Forest, Neural Networks, Decision Trees, and Linear Regression. The findings underscore the superior predictive capabilities of ensemble methods such as Random Forests and XGBoost, which outperform individual models. Simpler approaches like Decision Trees and k-NN offer effective yet slightly less precise alternatives. Neural Networks, despite their complexity, highlight the potential for further refinement in this application. In addition, our results show that the machine learning models significantly outperform the linear regression baseline, demonstrating the added value of more complex techniques in this domain. Our SHAP analysis reveals that the feature importance rankings in tree-based machine learning models differ significantly from those in k-NN, neural network, and linear regression models.
AB - The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, enhancing the field of environmental monitoring with the aid of sensor-based data collection. Our machine learning models consist of a novel and comprehensive dataset collected from a network of strategically placed sensors, capturing not only electromagnetic field readings but also additional urban features, including population density, levels of urbanization, and specific building characteristics. This sensor-driven approach, coupled with explainable AI, enables us to identify key factors influencing electromagnetic exposure more accurately. The integration of IoT sensor data with machine learning opens the potential for creating highly detailed and dynamic electromagnetic pollution maps. These maps are not merely static snapshots; they offer researchers the ability to track trends over time, assess the effectiveness of mitigation efforts, and gain a deeper understanding of electromagnetic field distribution in urban environments. Through the extensive dataset, our models can yield highly accurate and dynamic electric field strength maps. For this study, we performed a comprehensive analysis involving 566 machine learning models across eight French cities: Lyon, Saint-Étienne, Clermont-Ferrand, Dijon, Nantes, Rouen, Lille, and Paris. The analysis incorporated six core approaches: k-Nearest Neighbors, XGBoost, Random Forest, Neural Networks, Decision Trees, and Linear Regression. The findings underscore the superior predictive capabilities of ensemble methods such as Random Forests and XGBoost, which outperform individual models. Simpler approaches like Decision Trees and k-NN offer effective yet slightly less precise alternatives. Neural Networks, despite their complexity, highlight the potential for further refinement in this application. In addition, our results show that the machine learning models significantly outperform the linear regression baseline, demonstrating the added value of more complex techniques in this domain. Our SHAP analysis reveals that the feature importance rankings in tree-based machine learning models differ significantly from those in k-NN, neural network, and linear regression models.
KW - electric field strength
KW - explainable artificial intelligence
KW - IoT sensors
KW - machine learning
KW - radio environment map
KW - urban areas
UR - http://www.scopus.com/inward/record.url?scp=85214530375&partnerID=8YFLogxK
U2 - 10.3390/s25010053
DO - 10.3390/s25010053
M3 - Article
C2 - 39796843
AN - SCOPUS:85214530375
SN - 1424-8220
VL - 25
JO - Sensors
JF - Sensors
IS - 1
M1 - 53
ER -