Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach
Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach
复制标题
使用人工神经网络方法预测城市主干道附近每小时空气污染物浓度
DOI：
10.1016/j.trd.2008.10.004
复制
发表时间：
2009-01-01
影响因子：
7.6
通讯作者：
Xie, Min
中科院分区：
文献类型：
--
作者：
Cai, Ming;Yin, Yafeng;Xie, Min
本文应用人工神经网络方法对广州市某干道附近空气污染物逐时浓度进行预测。影响污染物浓度的因素分为四类：交通相关、背景浓度、气象和地理。这些影响因素和一氧化碳，二氧化氮，特殊物质和臭氧浓度的小时平均值测量在三个选定的地点附近的动脉使用车载自动监测设备。使用收集的数据对基于反向传播神经网络的模型进行了训练、验证和测试。结果表明，该模型能够提前10 h以上分别对污染物的小时浓度进行准确的预测。对比研究表明，神经网络模型优于多元线性回归模型和加州线源扩散模型。(C)2008爱思唯尔有限公司版权所有。
This paper applies artificial neural network to predict hourly air pollutant concentrations near an arterial in Guangzhou, China. Factors that influence Pollutant concentrations are classified into four categories: traffic-related, background concentration, meteorological and geographical. The hourly averages of these influential factors and concentrations of carbon monoxide, nitrogen dioxide, particular matter and ozone were measured at three selected sites near the arterial using vehicular automatic monitoring equipments. Models based on back-propagation neural network were trained, validated and tested using the collected data. It is demonstrated that the models are able to produce accurate prediction of hourly concentrations of the pollutants respectively more than 10 h in advance. A comparison study shows that the neural network models outperform multiple linear regression models and the California line source dispersion model. (C) 2008 Elsevier Ltd. All rights reserved.