对神经网络污染预报建模的影响因素

Impact of the Factors on Building Neural Network Model on Air Pollution Forecast

摘要: 为了通过大气环境质量的预测提出有效的污染防治措施,作者采用伦敦市PM_2.5的小时监测数据,利用传统的BP神经网络建立预报模型,定量预测伦敦市PM_2.5的小时平均质量浓度,探讨了大气污染预报网络的建模过程中,扩大样本集、去除样本集数据噪声和在输入向量中加入气象变量等因素对建模所产生的影响.最后得出结论,适当选择样本集、加入气象变量,有利于提高所建网络模型的预测精度.

Abstract: This paper uses the data of hourly concentration of PM_2.5 in London and the traditional BP neural network to build forecast model and to quantitively forecast the hourly concentration of PM_2.5 in London, discusses the impacts of enlarging sample data, reducing noise of sample data and adding weather factors in inputing vector on setting up the model of the air pollution forecast network.Finally, it comes to the conclusion that properly selecting sample data and adding weather factors is beneficial to improving forecasting precision of the network model.