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首页> 外文会议>2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring >Study on Air Fine Particles Pollution Prediction of Main Traffic Route Using Artificial Neural Network
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Study on Air Fine Particles Pollution Prediction of Main Traffic Route Using Artificial Neural Network

机译：人工神经网络在主要交通路径中空气微粒污染预测中的应用

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In this paper the feasibility of artificial neural network technology for air fine particles pollution prediction of main traffic route was discussed. The concentration data of PM2.5, PM5 and PM10 were measured in Zhongshan road, the main traffic route of Chongqing, China. Parameter §¶ of emission capacity of motor vehicles was used as the independent variable of prediction model. RBF and BP neural network were used to simulate the concentration of fine particles of different sizes. The results show that: (1) Prediction results of PM of different sizes are different, the simulating data of PM2.5 using RBF networks are better than those of PM5 and PM10, (2) The simulation effect of RBF neural network is related to maximum nerve cell number of network and the distribution density of radial basis function. When the maximum nerve cell number is 13 and the distribution density of radial basis function is 0.9, the simulation result of PM2.5 is best, (3) Using three hidden layers and Levenberg-Marquardt calculation method of BP neural network, good simulation effect could be achieved, (4) For PM2.5, the correlation coefficient between simulating data of testing sample and testing data are 0.94 and 0.91, the ratio of training error and testing error are 0.75 and 1.59 each by RBF and BP neural network. All above show that PM2.5 of main traffic route come mainly from vehicle emission. The two neural network established herein can be used to predict pollution of PM2.5.
机译：讨论了人工神经网络技术在主要交通路线空气细颗粒物污染预测中的可行性。在重庆的主要交通路线中山路测量了PM2.5，PM5和PM10的浓度数据。机动车排放能力参数§¶被用作预测模型的自变量。使用RBF和BP神经网络来模拟不同大小的细颗粒的浓度。结果表明：（1）不同大小的PM的预测结果不同，使用RBF网络对PM2.5的仿真数据要好于PM5和PM10，（2）RBF神经网络的仿真效果与网络的最大神经细胞数和径向基函数的分布密度。当最大神经细胞数为13且径向基函数的分布密度为0.9时，PM2.5的模拟结果最佳。（3）使用三个隐藏层和BP神经网络的Levenberg-Marquardt计算方法，模拟效果好（4）对于PM2.5，通过RBF和BP神经网络，测试样本模拟数据与测试数据之间的相关系数分别为0.94和0.91，训练误差与测试误差之比分别为0.75和1.59。以上说明主要交通路线的PM2.5主要来自机动车排放。本文建立的两个神经网络可用于预测PM2.5的污染。

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