2026-04-17T23:13:55Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/72672023-01-19T01:05:24Zcom_10259.4_2526com_10259.4_2525com_10259.4_106com_10259_2604com_10259_3847com_10259_5086col_10259_4164col_10259_3848
Repositorio Institucional de la Universidad de Burgos
author
Arroyo Puente, Ángel
author
Herrero Cosío, Álvaro
author
Corchado, Emilio
author
Tricio Gómez, Verónica
2023-01-18T12:04:33Z
2023-01-18T12:04:33Z
2017-12
1367-0751
http://hdl.handle.net/10259/7267
10.1093/jigpal/jzx050
1368-9894
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.
eng
Hybrid systems
Clustering techniques
Air quality
Projection models
Artificial neural networks
A hybrid intelligent system for the analysis of atmospheric pollution: a case study in two European regions
info:eu-repo/semantics/article
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URL
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041bb76ecc078a91ca93baab2ada43e3
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Arroyo-igpl_2017.pdf