Knowledge extraction through time windows on atmospheric variables

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TICEC 2022
PDF (Spanish) MHT (Spanish)
Published : 2022-11-30
DOI : https://doi.org/10.37815/rte.v34n3.952
Keywords :
clustering, correlated, X-means, atmospheric pollutants, meteorological variables, time windows
Nicolas Alvarez
https://orcid.org/0000-0002-4649-6844
Jaime Panata
https://orcid.org/0000-0001-6233-4899
Marcos Orellana
https://orcid.org/0000-0002-3671-9362
Priscila Cedillo
https://orcid.org/0000-0002-6787-0655
Jorge Luis Zambrano-Martinez
https://orcid.org/0000-0002-5339-7860
Juan Fernando Lima
https://orcid.org/0000-0003-3500-3968
Abstract

Industrialization and the rapid growth of urban areas are alarmingly increasing the presence of air pollutants. These pollutants affect the quality of life of people and present an opportunity for study is created to determine the atmospheric behavior and relationship between meteorological variables present in the environment. Prior to this, rolling windows of time were applied to remove anomalous data. Next, variables were identified, and the data was segmented through the X-means algorithm. Also, two clusters that represent the relationships between pairs of variables and the temporality of the time windows. As a result, an inverse correlation of -0.78 was found between the ozone and dew point variables within the hours of the working day.

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Alvarez Palacios, F. N., Panata Rivera, J. I., Orellana, M., Cedillo Orellana, I. P., Zambrano-Martinez, J. L., & Lima, J. F. (2022). Knowledge extraction through time windows on atmospheric variables. Revista Tecnológica - ESPOL, 34(3), 72-83. https://doi.org/10.37815/rte.v34n3.952
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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