Abstract
Understanding the spatial and temporal patterns of environmental exposure to radio-frequency electromagnetic fields (RF-EMF) is essential for conducting risk assessments. These assessments aim to explore potential connections between RF-EMF exposure and its effects on human health, as well as on wildlife and plant life. Existing research has used different machine learning tools for EMF exposure estimation; however, a comparative analysis of these techniques is required to better understand their performance for real-world datasets. In this work, we present both finite and infinite-width convolutional network-based methods to estimate and assess EMF exposure levels from 70 real-world sensors in Lille, France. A comparative analysis has been conducted to analyze the performance of the methods' execution time and estimation accuracy. To improve estimation accuracy for higher-resolution grids, we utilized a preconditioned gradient descent method for kernel estimation. Root Mean Square Error (RMSE) is used as the evaluation criterion for comparing the performance of these deep learning models.
Abstract (translated)
理解射频电磁场（RF-EMF）的环境暴露在时空模式中的规律对于进行风险评估至关重要。这些评估旨在探索RF-EMF暴露与人类健康、野生动物和植物生命之间潜在联系的影响。现有的研究已经使用了不同的机器学习工具来估算电磁场（EMF）暴露，但是需要对这些技术进行全面分析以更好地了解它们处理实际数据集的表现。在这项工作中，我们提出了基于有限宽度和无限宽度卷积网络的方法来估计和评估法国里尔70个真实世界传感器的EMF暴露水平。进行了一次比较性分析来分析方法执行时间和估算准确性的表现。为了提高更高分辨率网格中的估算准确性，我们在核函数估计中使用了预处理梯度下降法。均方根误差（RMSE）被用作比较这些深度学习模型性能的标准。
URL
https://arxiv.org/abs/2504.07990
