2026-04-18T04:43:13Zhttps://riubu.ubu.es/oai/request

oai: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

Arroyo Puente, Ángel Herrero Cosío, Álvaro Corchado, Emilio Tricio Gómez, Verónica Hybrid systems Clustering techniques Air quality Projection models Artificial neural networks 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. 2023-01-18 2023-01-18 2017-12 info:eu-repo/semantics/article 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 Oxford University Press