Airborne particle pollution predictive model using Gated Recurrent Unit (GRU) deep neural networks

Developments in deep learning for time-series problems have shown promising results for data prediction. Particulate Matter equal or smaller than 10 mu m (PM10) have increased importance in the research field due to the negative impact in the respiratory system. PM10 particles show non-linear behavior, hence it is not an easy task to implement techniques to predict subsequent concentration of the particles in the atmosphere. This paper presents a forecasting model using gated Recurrent unit (GRU) and Long-Short Term Memory (LSTM) networks, which are types of a deep recurrent neural network (RNN). The predicted results of PM10 are presented using data of Mexico City as a case study, showing that this type of deep network is feasible for predicting the non-linearities of this type of data. Several experiments were carried out for 12, 24, 48, and 120 h prediction, showing that this method may be applied to accurately forecast the behavior of PM10.