Prediction of ozone concentration using back propagation neural network with a novel hybrid training algorithm

Author

Zhang, Wen-Yu ; Guo, Zhen-Hai ; Liu, Xin ; Wang, Jian-Zhou

Author_Institution

Key Lab. of Arid Climatic Change & Reducing Disaster of Gansu Province, Lanzhou Univ., Lanzhou, China

Volume

8

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

4176

Lastpage

4179

Abstract

This paper proposed a new hybrid forecasting model for the prediction of ozone concentrations in semi-arid area. It is based on chaotic, particle swarm optimization algorithm (CPSO) and back propagation (BP) neural network, called CPSO-BP neural network. The results show that the proposed hybrid model is superior to both the BP neural network and the regression model being tested. The hybrid model achieves 18.7% in root mean square error reduction compared to BP model, and 8.1% reduction compared to stepwise regression model. It could be a promising model on forecasting ozone concentration in semi-arid area.

Keywords

backpropagation; chaos; environmental science computing; neural nets; oxygen; particle swarm optimisation; regression analysis; CPSO-BP neural network; back propagation neural network; chaotic algorithm; hybrid forecasting model; hybrid training algorithm; ozone concentration forecasting prediction; particle swarm optimization algorithm; regression model; root mean square error reduction; stepwise regression model; Artificial neural networks; Atmospheric modeling; Biological system modeling; Data models; Forecasting; Particle swarm optimization; Predictive models; BP neural network; chaotic particle swarm optimization; ozone;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5583703

Filename

5583703