2026-05-07T07:56:03Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72612023-01-19T01:05:10Zcom_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 Tricio Gómez, Verónica Corchado, Emilio Woźniak, Michał 2023-01-18T07:59:26Z 2023-01-18T07:59:26Z 2018 1076-2787 http://hdl.handle.net/10259/7261 10.1155/2018/7238015 1099-0526 Ozone is one of the pollutants with most negative efects on human health and in general on the biosphere. Many data-acquisition networks collect data about ozone values in both urban and background areas. Usually, these data are incomplete or corrupt and the imputation of the missing values is a priority in order to obtain complete datasets, solving the uncertainty and vagueness of existing problems to manage complexity. In the present paper, multiple-regression techniques and Artifcial Neural Network models are applied to approximate the absent ozone values from fve explanatory variables containing air-quality information. To compare the diferent imputation methods, real-life data from six data-acquisition stations from the region of Castilla y Leon (Spain) are gathered ´ in diferent ways and then analyzed. Te results obtained in the estimation of the missing values by applying these techniques and models are compared, analyzing the possible causes of the given response. application/pdf eng Hindawi Complexity. 2018, V. 2018, p. 1-14 https://doi.org/10.1155/2018/7238015 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Informática Computer science Neural Models for Imputation of Missing Ozone Data in Air-Quality Datasets info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Complexity 2018 1 14