FORECAST OF THE CONCENTRATIONS OF PARTICULATE MATTER IN THE AIR (PM10) USING ARTIFICIAL NEURAL NETWORKS: CASE STUDY IN THE DISTRICT OF ATE, LIMA.

Abstract

The aim of this research was to evaluate the performance of the Artificial Neural Network (ANN) model to predict the concentrations of PM10 in the air, for which a case study was made for the district of Ate, Lima. For this, different ANN architectures were developed using as input data the records of air pollutants and meteorological variables obtained from the Air Quality Monitoring Station "ATE" and simulated data from the WRF-CHEM model. The different ANN architectures went through a training and verification process,
and their performance was evaluated using the Mean Square Error (MSE), precision (BIAS) and determination coefficient (R2). It was determined that the architecture that has a better performance had 19 neurons in the hidden layer, with values of 0,0230 for the ECM, 0,5308 for the BIAS and 0,823 for the R2, likewise, it can provide forecasts up to 6 hours in advance. This study can contribute to the implementation of Early Warning Systems (SAT) on possible increases in the air of PM10 concentrations.