The role of principal component analysis in the evaluation of air quality monitoring networks
One of the most statistical techniques used in environmental sciences is the Principal Component Analysis (PCA). This technique consist in a linear decomposition of a set of correlated variables into a set of uncorrelated variables named principal components. It is one of the simplest and most robus...
| Autor Principal: | Polanco Martínez, Josué M. |
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| Formato: | info:eu-repo/semantics/article |
| Idioma: | spa eng |
| Publicado: |
Universidad Santo Tomás
2016
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| Materias: | |
| Acceso en línea: |
http://revistas.usta.edu.co/index.php/estadistica/article/view/2654 |
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| Sumario: |
One of the most statistical techniques used in environmental sciences is the Principal Component Analysis (PCA). This technique consist in a linear decomposition of a set of correlated variables into a set of uncorrelated variables named principal components. It is one of the simplest and most robust ways of doing dimensionality reduction. The PCA is widely used in the study of environmental phenomena, from the analysis of meteorological fields to the evaluation of air quality monitoring networks (AQMN). Due to the potential use of this method, more information in Spanish is required. For these reasons, we are highly motivated to contribute with this review paper, which contains the state of the art to evaluate AQMN by means of PCA. Additionally, some examples (simulated and real-world data) are presented to exemplify the use of this technique. Keywords: principal component analysis, air quality monitoring networks, redundant sensor detection. |
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