Prediction of PM10 concentrations in the city of Agadir (Morocco) using non-linear autoregressive artificial neural networks with exogenous inputs (NARX)

Abstract

The PM10 (particulate matter with diameter that is less than or equal to 10 μm) is qualified as one of the most dangerous air pollutants, affecting negatively the human health by causing lung, throat and heart diseases. These facts accentuate the need of forecasting the aforementioned air pollutant in order to provide predictions that will help reducing the exposure to the PM10. In this present study, we attempt to forecast PM10 concentrations in Agadir City by employing non-linear autoregressive artificial neural networks with exogenous multi-variable inputs (NARX-ANN) 1-h and 24-h ahead. We have used different NARX-ANN architectures depending on the structure and the input parameters in order to find the most optimal ones. The performance and the quality of the models were evaluated using root mean square error (RMSE), coefficient of correlation (CC) and mean absolute error (MAE).