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Complexity
Volume 2018, Article ID 7238015, 14 pages
https://doi.org/10.1155/2018/7238015
Research Article

Neural Models for Imputation of Missing Ozone Data in Air-Quality Datasets

1Department of Civil Engineering, University of Burgos, Burgos, Spain
2Department of Physics, University of Burgos, Burgos, Spain
3Departamento de Informática y Automática, University of Salamanca, Salamanca, Spain
4Department of Systems and Computer Networks, Wrocław University of Science and Technology, Wrocław, Poland

Correspondence should be addressed to Ángel Arroyo; se.ubu@poyorraa

Received 5 December 2017; Accepted 31 January 2018; Published 8 March 2018

Academic Editor: Eloy Irigoyen

Copyright © 2018 Ángel Arroyo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Ozone is one of the pollutants with most negative effects 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 Artificial Neural Network models are applied to approximate the absent ozone values from five explanatory variables containing air-quality information. To compare the different imputation methods, real-life data from six data-acquisition stations from the region of Castilla y León (Spain) are gathered in different ways and then analyzed. The 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.