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人工智能-物联网低成本污染监测传感器网络,帮助患有呼吸系统问题的公民
Santiago Felici-Castell1, Jaume Segura-Garcia1, Juan J Perez-Solano1
1Escola Tècnica Superior d'Enginyeria, Universitat de València, Campus de Burjassot, 46100 Valencia, Spain.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究将低成本空气质量 (AQ) 传感器与5G和AI集成,以预测24小时的空气质量,从而实现早期公共卫生警告. 多长短期内存 (LSTM) 网络实现了最佳的预测准确性.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 传感器技术 传感器技术
背景情况:
- 低成本的空气质量 (AQ) 传感器为无线传感器网络 (WSN) 提供了灵活性和能源效率.
- 传统的AQ监测面临着数据复杂性和易出错的挑战,尽管低成本传感器可以提高空间和时间采样的潜力.
- 由于呼吸和过敏问题日益增加,城市地区越来越需要先进的AQ监测.
研究的目的:
- 将低成本的AQ传感器与5G通信和人工智能 (AI) 进行实时监控.
- 开发24小时前的AQ读数预测模型,以提供早期公共卫生警告.
- 评估用于AQ预测的不同神经网络架构的性能.
主要方法:
- 利用了低成本 AQ 传感器和 5G 通信基础设施的网络.
- 应用人工智能 (AI) 技术,特别是卷积神经网络 (CNN) 和深度神经网络 (DNN).
- 评估了四种神经网络架构:多线性,多密度,多卷积和多长短期记忆 (LSTM) 网络,在一个全面的数据集上进行训练.
主要成果:
- 对于大多数预测的AQ参数,平均估计误差约为7.2%.
- 多LSTM网络在24小时前的AQ预测中表现最佳.
- 具体的污染物显示出较低的误差:CO2 (0.1%),PM10 (2.4%),PM2.5 (2.4%),PM1.0 (2.4%),NO2 (6.7%) 等.
结论:
- 低成本AQ传感器,5G和AI (特别是多LSTM) 的整合对于准确的短期空气质量预测是有效的.
- 这种方法可以显著提高AQ监测,特别是在城市环境中,通过提供及时的早期警告.
- 该研究验证了先进的人工智能模型的潜力,以提高公共卫生应用中低成本传感器数据的实用性.
关键词:
这就是为什么物联网物联网物联网.这是LSTM的LSTM.WSN WSN 在线新闻空气污染 空气污染人工智能的人工智能是人工智能.预测 预测 预测 预测低成本的传感器神经网络的神经网络的神经网络更多相关视频
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