Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

Abstract

Understanding the behavior of suspended pollutants in the atmosphere has become of paramount importance to determine air quality. For this purpose, a variety of simulation software packages and a large number of algorithms have been used. Among these techniques, recurrent deep neural networks (RNN) have been used lately. These are capable of learning to imitate the chaotic behavior of a set of continuous data over time. In the present work, the results obtained from implementing three different RNNs working with the same structure are compared. These RNNs are long-short term memory network (LSTM), a recurrent gated unit (GRU), and the Elman network, taking as a case study the records of particulate matter PM10 and PM2.5 from 2005 to 2019 of Mexico City, obtained from the Red Automatica de Monitoreo Ambiental (RAMA) database. The results were compared for these three topologies in execution time, root mean square error (RMSE), and correlation coefficient (CC) metrics.