2026-04-17T23:14:33Zhttps://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

00925njm 22002777a 4500 dc Arroyo Puente, Ángel author Herrero Cosío, Álvaro author Corchado, Emilio author Tricio Gómez, Verónica author 2017-12 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. 1367-0751 http://hdl.handle.net/10259/7267 10.1093/jigpal/jzx050 1368-9894 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