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A hybrid intelligent system for the analysis of atmospheric pollution: a case study in two European regions

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Title:
A hybrid intelligent system for the analysis of atmospheric pollution: a case study in two European regions.
Authors: 
Arroyo Puente, Ángel; Herrero Cosío, Álvaro; Corchado Rodríguez, Emilio; Tricio, Verónica
Journal:
Logic Journal of the IGPL. Volume 25 (6), pp. 915-937. Oxford University Press.

Publication date: 
1 December 2017
ISSN: 
1367-0751 (Print), 1368-9894 (Online)
DOI
 10.1093/jigpal/jzx050

BibTex

@article { article,
title = {A hybrid intelligent system for the analysis of atmospheric pollution: a case study in two European regions},
author = {Arroyo Puente, Ángel; Herrero Cosío, Álvaro; Corchado Rodríguez, Emilio; Tricio, Verónica },
journal = {Logic Journal of the IGPL},
publisher = {Oxford University Press},
volume = {25},
number = {6},
year = {2017}
}

XML

<article key='journals/Logic/Arroyo/1 December 2017' mdate='1 December 2017'>
<author> Arroyo Puente</author>
<author> Ángel; Herrero Cosío</author>
<author> Álvaro; Corchado Rodríguez</author>
<author> Emilio; Tricio</author>
<author> Verónica</author>
<title> A hybrid intelligent system for the analysis of atmospheric pollution: a case study in two European regions</title>
<pages> 915-937</pages>
<year> 2017</year>
<journal> Logic Journal of the IGPL</journal>
<ee> 10.1093/jigpal/jzx050</ee>
</article>
Evidences of quality:
JCR(2016): 0.575
MATHEMATICS, APPLIED: 207/255 (Q4) MATHEMATICS: 181/311 (Q3)

The combined application of several soft-computing and statistical techniques is proposed for the characterization of atmospheric conditions in two European regions: Madrid (Spain) and Prague (Czech Republic). The resulting Hybrid Artificial Intelligence System (HAIS) combines projection models for dimensionality reduction and clustering, combining neural and fuzzy paradigms, in a decision support tool. In present article, this proposed HAIS is applied to analyse the air quality in these two geographical regions and get a better understanding of its circumstances and evolution. To do so, real-life data from six data-acquisition stations are analysed. The main pollutants recorded at these stations between 2007 and 2014, their geographical locations and seasonal changes are all studied, in a research that shows how such factors determine variations in air-borne pollutants. Furthermore, neural projections of the clustering results from data on atmospheric pollution are studied.

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