Forecasting particulate air pollution (PM10) using a Machine Learning K-Nearest Neighbors
DOI:
https://doi.org/10.18041/2322-8415/ingelibre.2024.v13n23.11352Keywords:
Contaminación del aire, Predicción, Material Particulado (PM10), KNN, KennedyAbstract
Currently, air pollution is an issue that has become increasingly relevant, since it has increased due to
urbanization, industrialization, among others; Furthermore, it is a serious problem that affects people's health and
the environment. It is caused by the emission of toxic gases caused either by industrial and/or anthropogenic
activities. Air quality has become a global concern, and measures are required to reduce emissions and improve
air quality around the world. This research made it possible to predict the values of PM10 particulate matter in
the town of Kennedy in Bogotá with the help of the K-Nearest Neighbors Machine Learning algorithm, in
addition, obtaining encouraging results regarding the generalization of the test data, demonstrating good
reliability regarding its predictions.
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