Nineteenth International Conference on Interdisciplinary Social Sciences

Abstract

Over time, predictive models tend to become more accurate, but also more complex, thus achieving better predictive accuracy. When the data is improved by increasing its quantity and availability, the models are also better, which implies that the data must be processed to filter and adapt it for initial analysis and then modelling. The aim of this work is to apply the Random Forest model to predict PM10 particles. For this purpose, data were obtained from environmental monitoring stations in Mexico City, which operates 29 stations of which 12 belong to the State of Mexico. The pollutants analysed were CO carbon monoxide, NO nitrogen oxide, PM10 particulate matter equal to or less than 10 µg/m3, NOx nitrogen oxide, NO2 nitrogen dioxide, SO2 sulphur dioxide, O3 ozone and PM2.5 particulate matter equal to or less than 2.5 µg/m3. The result was that when calculating the certainty of our model we have a value of 80.40 % calculating the deviation from the mean, using 15 reference variables.