使用层循环神经网络模型进行 AQI 预测:一种新方法.
1Department of Civil Engineering, Bharat Institute of Engineering and Technology, Hyderabad, Telangana, India.
Environmental monitoring and assessment
|September 10, 2023
概括
预测每日空气质量指数 (AQI) 提前一年对公共卫生至关重要. 在预测AQI方面,LR-NN模型表现出卓越的准确性,优于FF-NN和CF-NN模型.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 大气化学 大气化学
背景情况:
- 在德里,空气中的污染物对人类健康构成重大威胁.
- 准确的空气质量指数 (AQI) 评估对于评估污染物影响至关重要.
- 现有的方法需要改进,以准确,长期的AQI预测.
研究的目的:
- 预测德里的每日空气质量指数 (AQI) 提前一年.
- 为了比较三个神经网络模型的预测性能:FF-NN,CF-NN和LR-NN.
- 确定影响每日AQI水平的关键空气污染物.
主要方法:
- 使用2019 AQI数据开发和训练了三个神经网络模型 (FF-NN,CF-NN,LR-NN).
- 纳入的主要污染物 (PM2.5/PM10,O3,SO2,NOx,CO,NH3),非标准污染物和气象数据作为输入.
- 使用统计分析评估模型性能,重点关注根平均平方误差 (RMSE) 和R平方 (R2).
主要成果:
- 该LR-NN模型实现了最低的RMSE (26.79),显示出优异的预测准确度.
- PM2.5/PM10,CO和NO2被确定为每日高AQI的主要贡献者.
- LR-NN在所有季节都表现出强的表现,特别是在季风后的时期 (R2 = 0.94).
结论:
- 该LR-NN模型提供了一个精确的方法,用于一年前的AQI预测.
- 调查结果可以帮助空气污染控制当局及时实施有效的减缓战略.
- 建议进行进一步的研究,将LR-NN与其他建模方法进行比较,以进行全面的空气质量评估.
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