DocumentCode :
478038
Title :
A BP Neural Network Prediction Model of the Urban Air Quality Based on Rough Set
Author :
Jiang, Zhifang ; Meng, Xiangxu ; Yang, Chenglei ; Li, Guansong
Author_Institution :
Sch. of Comput. Sci. & Technol., Shandong Univ., Jinan
Volume :
1
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
362
Lastpage :
370
Abstract :
The paper gives a BP neural network (BPNN) prediction model of the ambient air quality based on rough set theory. We make first the reduction of monitoring data of the pollution sources using the theory of rough set, extract the tidy rules. Then the topological structure of the multilayer BPNN and the nerve cells of the connotative layer are defined with these rules. After that the connected weight values of corresponding nodes of the BPNN are ascertained. Using BP arithmetic, the prediction model is trained with the monitoring data of the pollution sources and air monitor stations for gaining the various parameters of it. Finally, the model after training is used to predict the urban air quality with certain meteorological parameters. The result of the prediction model was proved that it is more accurate than the common BPNN.
Keywords :
air pollution; backpropagation; environmental science computing; neural nets; prediction theory; rough set theory; BP neural network prediction model; multilayer BPNN; pollution sources; rough set theory; urban air quality; Air pollution; Arithmetic; Condition monitoring; Data mining; Feedforward neural networks; Meteorology; Neural networks; Pattern recognition; Predictive models; Set theory; Air quality; BP neural network; Prediction model; Reduction; Rough set;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
Type :
conf
DOI :
10.1109/ICNC.2008.807
Filename :
4666870
Link To Document :
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