Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong KongReportar como inadecuado


Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong


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1

School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China

2

School of Mathematics and Information, BeiFang University of Nationalities, Yinchuan 750021, China





*

Author to whom correspondence should be addressed.



Academic Editor: Paul B. Tchounwou

Abstract With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively. View Full-Text

Keywords: feed forward neural network; air pollution; back propagation; extreme learning machine; prediction feed forward neural network; air pollution; back propagation; extreme learning machine; prediction





Autor: Jiangshe Zhang 1,* and Weifu Ding 1,2

Fuente: http://mdpi.com/



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