对八个欧洲城市电场强度预测可解释的人工智能模型的比较分析
Yiannis Kiouvrekis1,2,3, Ioannis Givisis1, Theodor Panagiotakopoulos4
1Mathematics, Computer Science and Artificial Intelligence Lab, Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
机器学习模型,包括随机森林和XGBoost,使用传感器数据准确地绘制城市电场强度. 这促进了环境监测和对电磁场暴露的理解.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 无线设备的扩散引发了人们对电磁场 (EMF) 暴露的担忧.
- 准确地绘制EMF对环境监测和公共卫生评估至关重要.
研究的目的:
- 确定最佳的机器学习模型,用于在城市地区创建详细的电场强度图.
- 利用传感器数据和可解释的人工智能来增强对电磁场分布的理解.
主要方法:
- 从测量EMF,人口密度,城市化和建筑特征的传感器收集了一个新的数据集.
- 在法国八个城市分析了566个机器学习模型 (k-NN,XGBoost,随机森林,神经网络,决策树,线性回归).
- 使用SHAP分析来确定特征的重要性和模型可解释性.
主要成果:
- 集合方法,特别是随机森林和XGBoost,比单个模型显示出优越的预测性能.
- 机器学习模型的表现明显超过了线性回归基线.
- 特征的重要性在基于树的模型和其他方法 (如k-NN和神经网络) 之间显著不同.
结论:
- 机器学习,特别是组合方法,为动态电磁污染映射提供了强大的工具.
- 传感器驱动的数据和可解释的AI提高了EMF暴露评估的准确性和可解释性.
- 结果为跟踪EMF趋势和评估城市环境中的减缓策略提供了基础.
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