基于深度学习的多式联运城市空气质量预测和交通分析
Saad Hameed1, Ashadul Islam1, Kashif Ahmad2
1Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Scientific reports
|December 13, 2023
概括
准确的空气质量预测现在可以使用一个新的AI框架,集成环境传感器数据和交通密度. 该系统可以改善城市污染的预测,帮助公共卫生和环境管理.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 城市活动,特别是车辆流量,大大导致环境污染,并对公众健康产生负面影响.
- 准确的空气质量预测对当局和公众来管理城市活动和减轻不利影响至关重要.
- 人工智能和传感器技术的进步通过整合各种环境因素,使复杂的空气质量预测成为可能.
研究的目的:
- 为空气质量预测提供一个新的多模式框架,将环境传感器数据和交通密度整合起来.
- 为了解决传感器/摄像头故障和流数据集中的现实世界复杂性的数据不一致问题.
- 开发一种能够预测传感器覆盖范围有限或失败的地区空气质量的系统.
主要方法:
- 集成来自环境传感器的实时数据和从闭路电视 (CCTV) 录像中提取的交通密度.
- 开发一个多模式框架来处理数据不一致性,噪声和流数据集中的异常值.
- 利用基于粒子群集优化 (PSO) 的优点融合来训练来自附近传感器的数据的联合模型,以预测未监测地点的空气质量.
主要成果:
- 拟议的框架有效地整合了各种数据源,并解决了现实世界的数据复杂性.
- 使用长短期记忆 (LSTM) 变体 (双向LSTM,CNN-LSTM,ConvLSTM) 进行评估,该系统显示出显著的预测改进.
- 与ARIMA模型相比,实现的百分比改善:48% (短期),67% (中期) 和173% (长期).
结论:
- 这种由人工智能驱动的新型框架为城市环境中准确的空气质量预测提供了强大的解决方案.
- 该系统能够处理数据不一致并预测未被监控区域的能力提高了其实际适用性.
- 通过LSTM变体实现的显著改进突显了先进AI在环境监测和公共卫生保护方面的潜力.
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