以人工智能为基础,预测紧急措施导致的空气质量改善
Pavithra Pari1, Tasneem Abbasi1, S A Abbasi1
1Centre for Pollution Control and Environmental Engineering, Pondicherry University, Pondicherry, 605014, India.
Journal of environmental management
|December 8, 2023
概括
这项研究引入了一种新的人工神经网络模型,以预测当污染源暂时停止时,空气质量会有多快地改善. 该模型准确预测PM2.5水平的降低,有助于实现安全的空气质量标准.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 数据科学数据科学数据科学
背景情况:
- 像新德里这样的发展中世界城市经常面临危险的空气污染水平.
- 暂时的减排是常见的,但缺乏准确的预测有效性.
研究的目的:
- 开发和验证一个人工神经网络模型,用于预测空气质量参数变化.
- 预测在排放停止后达到安全PM2.5水平所需的时间.
主要方法:
- 使用非线性自回归外源 (NAUX) 网络模型.
- 输入参数包括自排放停止以来的时间,湿度,风速/方向和温度.
- 在COVID-19封锁期间使用新德里的广泛空气质量数据进行了模型的训练和测试.
主要成果:
- 该模型准确地预测了PM2.5下降的速度,在培训和测试期间具有高R和低MSE.
- 在19个新德里站的验证显示,预测和测量的PM2.5.5之间存在很强的相关性 (0.74-0.94).
- 测试期间的平均平方误差 (MSE) 值在0.0110至1.0746,表明高精度.
结论:
- 开发的模型可以有效地确定排放停止所需的持续时间,以达到安全的PM2.5水平.
- 该方法可适应在全球其他地区创建类似的空气质量预测模型.
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