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Atmosphere 2015, 6(7), 891-907; doi:10.3390/atmos6070891

Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule

1
School of Engineering, Sun Yat-Sen University, Guangzhou 510275, China
2
Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China
3
Institute of Advanced Technology, Sun Yat-Sen University, Guangzhou 510275, China
*
Author to whom correspondence should be addressed.
Academic Editor: Rebecca Sheesley
Received: 28 April 2015 / Revised: 15 June 2015 / Accepted: 24 June 2015 / Published: 9 July 2015
(This article belongs to the Special Issue Air Quality and Source Apportionment)
View Full-Text   |   Download PDF [758 KB, uploaded 9 July 2015]   |  

Abstract

In this paper, based on a sample selection rule and a Back Propagation (BP) neural network, a new model of forecasting daily SO2, NO2, and PM10 concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO2, NO2, and PM10 daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM10 displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE) values decreasing by 4% and R2 values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale. View Full-Text
Keywords: daily SO2; NO2; PM10 concentration; a selection sample rule; BP neural network; meteorological similarity; variation consistency daily SO2; NO2; PM10 concentration; a selection sample rule; BP neural network; meteorological similarity; variation consistency
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Liu, Y.; Zhu, Q.; Yao, D.; Xu, W. Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule. Atmosphere 2015, 6, 891-907.

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