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Int. J. Environ. Res. Public Health 2017, 14(3), 249; doi:10.3390/ijerph14030249

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
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Academic Editor: Michael S. Breen
Received: 30 November 2016 / Revised: 11 February 2017 / Accepted: 24 February 2017 / Published: 2 March 2017
(This article belongs to the Section Global Health)
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Abstract

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable. View Full-Text
Keywords: air quality; early warning system; forecasting; comprehensive evaluation air quality; early warning system; forecasting; comprehensive evaluation
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MDPI and ACS Style

Wang, J.; Niu, T.; Wang, R. Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model. Int. J. Environ. Res. Public Health 2017, 14, 249.

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