计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (8): 199-201.DOI: 10.3778/j.issn.1002-8331.2010.08.057

• 工程与应用 • 上一篇    下一篇

环境空气SO2和NO2浓度的GA_ANN预测模型研究

赵 宏1,3,4,刘爱霞2,王 恺1,3,4,白志鹏1,3   

  1. 1.南开大学 环境科学与工程学院,天津 300071
    2.天津市气象科学研究所,天津 300074
    3.国家环境保护城市空气颗粒物污染防治重点实验室,天津 300071
    4.南开大学 信息技术科学学院,天津 300071
  • 收稿日期:2008-09-12 修回日期:2008-11-27 出版日期:2010-03-11 发布日期:2010-03-11
  • 通讯作者: 赵 宏

Study on GA_ANN based ambient air SO2 and NO2 concentration forecasting model

ZHAO Hong1,3,4,LIU Ai-xia2,WANG Kai1,3,4,BAI Zhi-peng1,3   

  1. 1.College of Environmental Science and Engineering,Nankai University,Tianjin 300071,China
    2.Meteorological Institute of Tianjin,Tianjin 300074,China
    3.State Environmental Protection Key Lab of Urban Ambient Air Particulate Matter Pollution Prevention & Control,Tianjin 300071,China
    4.College of Information Technical Science,Nankai University,Tianjin 300071,China
  • Received:2008-09-12 Revised:2008-11-27 Online:2010-03-11 Published:2010-03-11
  • Contact: ZHAO Hong

摘要: 空气中污染物浓度的预测是一个复杂的非线性问题。国内外的研究表明神经网络能够比回归模型更好地预报空气污染物。设计并实现了将用于选择最优预报因子的遗传算法和神经网络算法相结合的GA_ANN空气质量预测模型,利用某市2003~2006年的数据建立神经网络空气质量预测模型,对该市2007年全年SO2和NO2的预测实验表明,GA_ANN模型比单纯的神经网络模型具有更高的预报精度。

Abstract: The concentration prediction of the air pollutants is a complicated issue with nonlinear feature.Previous researches indicate that compared with the normal step wise regress method,the forecast precision with the ANN(Artificial Neural Networks) method is greatly improved.The GA_ANN air quality forecasting model is developed,which integrates Genetic Algorithm and Neural Network Algorithm.Genetic Algorithm is employed to select the most suitable forecasting factors for better performance. Some city air quality monitoring data and meteorological data from 2003 to 2006 are chosen to establish Neural Network of the GA_ANN model.The forecasting experiments about SO2 and NO2 of some city in 2007 show that the GA_ANN model has a higher forecasting precision than normal Neural Network model.

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