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Predicting Common Air Quality Index – The Case of Czech Microregions

Category: Aerosol Chemistry and Urban Air Quality

Volume: 15 | Issue: 2 | Pages: 544-555
DOI: 10.4209/aaqr.2014.08.0154
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Petr Hajek , Vladimir Olej

  • Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Studentská 84, 532 10 Pardubice, Czech Republic

Highlights

We employ TSFIS, RBF and MLP neural networks, and SVR for AQIs’ prediction. 

Non-linearity in the data can be effectively processed and modeled by these methods. 

The methods are compared on three monitoring stations with regard to RMSE.

We perform feature selection for the stations to detect the determinants of AQIs.


Abstract

This paper presents a design of models for common air quality index prediction using computational intelligence methods. In addition, the sets of input variables were optimized for each air pollutant prediction by genetic algorithms. Based on data measured by the three monitoring stations of Dukla, Rosice and Brnenska in the Czech Republic, the models were designed to predict air quality indices for each air pollutant separately and, consequently, to predict the common air quality index. Considering the root mean squared error, the results showed that the compositions of individual prediction models significantly outperform single prediction models of the common air quality index. The feature selection procedure indicates that the determinants of air quality indices were strongly locality specific. Therefore, the models can be applied to obtain more accurate one day ahead predictions of air quality indices. Here we show that the composition models achieve high prediction accuracy for maximum air quality indices (between 50.69 and 63.36%). The goal of the prediction by various methods was to compare the results of the prediction with the aim of various recommendations to micro-regional public administration management.

Keywords

Air quality index Prediction Fuzzy inference system Neural network Support vector regression


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