A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities
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 pr...
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MDPI AG
2024-12-01
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| author | Yiannis Kiouvrekis Ioannis Givisis Theodor Panagiotakopoulos Ioannis Tsilikas Agapi Ploussi Ellas Spyratou Efstathios P. Efstathopoulos |
| author_facet | Yiannis Kiouvrekis Ioannis Givisis Theodor Panagiotakopoulos Ioannis Tsilikas Agapi Ploussi Ellas Spyratou Efstathios P. Efstathopoulos |
| author_sort | Yiannis Kiouvrekis |
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| description | 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. |
| format | Article |
| id | doaj-art-95831f08b8ce4bffa6146e5fd90f379c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
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| series | Sensors |
| spelling | doaj-art-95831f08b8ce4bffa6146e5fd90f379c2025-01-10T13:20:42ZengMDPI AGSensors1424-82202024-12-012515310.3390/s25010053A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European CitiesYiannis Kiouvrekis0Ioannis Givisis1Theodor Panagiotakopoulos2Ioannis Tsilikas3Agapi Ploussi4Ellas Spyratou5Efstathios P. Efstathopoulos6Mathematics, Computer Science and Artificial Intelligence Lab, Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, GreeceMathematics, Computer Science and Artificial Intelligence Lab, Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, GreeceDepartment of Management Science and Technology, University of Patras, 26334 Patras, GreeceDepartment Applied Physics and Mathematics, National Technical University of Athens, Iroon Polytechniou 9, Zografou, 15772 Athens, Greece2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 12462 Athens, GreeceThe 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.https://www.mdpi.com/1424-8220/25/1/53machine learningexplainable artificial intelligenceelectric field strengthradio environment mapurban areasIoT sensors |
| spellingShingle | Yiannis Kiouvrekis Ioannis Givisis Theodor Panagiotakopoulos Ioannis Tsilikas Agapi Ploussi Ellas Spyratou Efstathios P. Efstathopoulos A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities Sensors machine learning explainable artificial intelligence electric field strength radio environment map urban areas IoT sensors |
| title | A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities |
| title_full | A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities |
| title_fullStr | A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities |
| title_full_unstemmed | A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities |
| title_short | A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities |
| title_sort | comparative analysis of explainable artificial intelligence models for electric field strength prediction over eight european cities |
| topic | machine learning explainable artificial intelligence electric field strength radio environment map urban areas IoT sensors |
| url | https://www.mdpi.com/1424-8220/25/1/53 |
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