Prediction of atmospheric pollution using neural networks model of fine particles in the town of Kennedy in Bogota

Juan Camilo Pedraza (Universidad Distrital Francisco José de Caldas)
Oswaldo Alberto Romero (Universidad Distrital Francisco José de Caldas)
Helbert Eduardo Espitia (Universidad Distrital Francisco José de Caldas)

International Journal of Electrical and Computer Engineering
Vol 10, No 6: December 2020

This work shows an application based on neural networks to determine the prediction of air pollution, especially particulate material of 2.5 micrometers length. This application is considered of great importance due to the impact on human health and high impact due to the agglomeration of people in cities. The implementation is performed using data captured from several devices that can be installed in specific locations for a particular geographical environment, especially in the locality of Kennedy in Bogotá. The model obtained can be used for the design of public policies that control air quality.