Neural Models for Imputation of Missing Ozone Data in Air-Quality Datasets
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 pr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/7238015 |
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| author | Ángel Arroyo Álvaro Herrero Verónica Tricio Emilio Corchado Michał Woźniak |
| author_facet | Ángel Arroyo Álvaro Herrero Verónica Tricio Emilio Corchado Michał Woźniak |
| author_sort | Ángel Arroyo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-185900f248af4620b44993f093b4ffaa |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-185900f248af4620b44993f093b4ffaa2025-08-20T02:02:16ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/72380157238015Neural Models for Imputation of Missing Ozone Data in Air-Quality DatasetsÁngel Arroyo0Álvaro Herrero1Verónica Tricio2Emilio Corchado3Michał Woźniak4Department of Civil Engineering, University of Burgos, Burgos, SpainDepartment of Civil Engineering, University of Burgos, Burgos, SpainDepartment of Physics, University of Burgos, Burgos, SpainDepartamento de Informática y Automática, University of Salamanca, Salamanca, SpainDepartment of Systems and Computer Networks, Wrocław University of Science and Technology, Wrocław, PolandOzone 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.http://dx.doi.org/10.1155/2018/7238015 |
| spellingShingle | Ángel Arroyo Álvaro Herrero Verónica Tricio Emilio Corchado Michał Woźniak Neural Models for Imputation of Missing Ozone Data in Air-Quality Datasets Complexity |
| title | Neural Models for Imputation of Missing Ozone Data in Air-Quality Datasets |
| title_full | Neural Models for Imputation of Missing Ozone Data in Air-Quality Datasets |
| title_fullStr | Neural Models for Imputation of Missing Ozone Data in Air-Quality Datasets |
| title_full_unstemmed | Neural Models for Imputation of Missing Ozone Data in Air-Quality Datasets |
| title_short | Neural Models for Imputation of Missing Ozone Data in Air-Quality Datasets |
| title_sort | neural models for imputation of missing ozone data in air quality datasets |
| url | http://dx.doi.org/10.1155/2018/7238015 |
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