| 基于改进型灰色神经网络组合模型的空气质量预测 |
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| 引用本文： | 司志娟,孙宝盛,李小芳.基于改进型灰色神经网络组合模型的空气质量预测[J].环境污染治理技术与设备,2013(9):3543-3547. |
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| 作者姓名： | 司志娟 孙宝盛 李小芳 |
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| 作者单位： | [1]天津大学环境科学与工程学院,天津300072 [2]航天环境工程有限公司,北京100074 |
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| 摘 要： | 基于空气质量数据不足及波动较大的情况,将灰色GM（1,1）模型与人工神经网络模型组合并改进,建立改进型灰色神经网络组合模型。利用天津市2001—2008年PM10、SO2和NO2年均值作为原始数据预测2009—2010年PM10、SO2和NO2的浓度以进行模型精度检验,最后利用该模型预测2011—2015年天津市空气质量状况。结果表明,与灰色GM（1,1）模型、传统灰色神经网络组合模型相比,所建立的改进型灰色神经网络组合模型相对模拟误差小,预测结果更为可靠,可以用于空气质量预测。
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| 关 键 词： | 灰色GM（1 1）模型 传统灰色神经网络组合模型 改进型灰色神经网络组合模型 预测 空气质量 |
Prediction of air quality based on improved grey neural network model |
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| Si Zhijuan,Sun Baosheng,Li Xiaofang.Prediction of air quality based on improved grey neural network model[J].Techniques and Equipment for Environmental Pollution Control,2013(9):3543-3547. |
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| Authors: | Si Zhijuan Sun Baosheng Li Xiaofang |
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| Institution: | 1. School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China; 2. Aerospace Environmental Engineering Limited Company, Beijing 100074, China) |
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| Abstract: | In view of the air quality data shortage and high fluctuation,we combined grey model（GM（1,1）） with artificial neural network to establish an integration model.Then,improved grey neural network model（IGNNM） was presented by improving this combination model.As raw data,the annual average value of PM 10,SO 2 and NO 2 from 2001 to 2008 in Tianjin City was used to simulate.Meanwhile,the concentrations of PM 10,SO 2 and NO 2 in 2009 to 2010 were forecasted to check the precision of this model.Finally,the air quality of Tianjin between 2011 and 2015 was predicted by using this proposed model.The results show that the model mentioned can be employed in air quality prediction as its less relative simulation error and higher reliability,compared with grey model and traditional grey neural network model. |
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| Keywords: | grey model traditional grey neutral network model improved grey neural network model（IGNNM） prediction air quality |
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