2026-04-17T23:16:32Zhttps://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
A hybrid intelligent system for the analysis of atmospheric pollution: a case study in two European regions
Arroyo Puente, Ángel
Herrero Cosío, Álvaro
Corchado, Emilio
Tricio Gómez, Verónica
Hybrid systems
Clustering techniques
Air quality
Projection models
Artificial neural networks
Informática
Computer science
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.
2023-01-18T12:04:33Z
2023-01-18T12:04:33Z
2017-12
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
1367-0751
http://hdl.handle.net/10259/7267
10.1093/jigpal/jzx050
1368-9894
eng
Logic Journal of the IGPL. 2017, V. 25, n. 6, p. 915-937
https://doi.org/10.1093/jigpal/jzx050
info:eu-repo/semantics/openAccess
application/pdf
Oxford University Press
https://riubu.ubu.es/bitstream/10259/7267/3/Arroyo-igpl_2017.pdf.jpg
Hispana
TEXT
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RIUBU. Repositorio Institucional de la Universidad de Burgos
http://hdl.handle.net/10259/7267