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

Received: August 1, 2014
Revised: November 8, 2014
Accepted: January 4, 2015
Download Citation: ||https://doi.org/10.4209/aaqr.2014.08.0154  

  • Download: PDF


Cite this article:
Hajek, P. and Olej, V. (2015). Predicting Common Air Quality Index – The Case of Czech Microregions. Aerosol Air Qual. Res. 15: 544-555. https://doi.org/10.4209/aaqr.2014.08.0154


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


Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

6.5
2021CiteScore
 
 
77st percentile
Powered by
Scopus
 
   SCImago Journal & Country Rank

2021 Impact Factor: 4.53
5-Year Impact Factor: 3.668

Aerosol and Air Quality Research partners with Publons

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.