Performance evaluation of a recurrent deep neural network optimized by swarm intelligent techniques to model particulate matter

Javier Kuri-Monge, Gerardo; Antonio Aceves-Fernandez, Marco ✉; Carlos Pedraza-Ortega, Jesus

Angol nyelvű Szakcikk (Folyóiratcikk) Tudományos
  • SJR Scopus - Atmospheric Science: Q2
  • Föld- és kapcsolódó környezettudományok
  • Környezetmérnöki tudományok
Atmospheric pollution refers to the presence of substances in the air such as particulate matter (PM) which has a negative impact in population's health exposed to it. This makes it a topic of current interest. Since the Metropolitan Zone of the Valley of Mexico's geographic characteristics do not allow proper ventilation and due to its population's density a significant quantity of poor air quality events are registered. This paper proposes a methodology to improve the forecasting of PM10 and PM2.5, in largely populated areas, using a recurrent long-term/short-term memory (LSTM) network optimized by the Ant Colony Optimization (ACO) algorithm. The experimental results show an improved performance in reducing the error by around 13.00% in RMSE and 14.82% in MAE using as reference the averaged results obtained by the LSTM deep neural network. Overall, the current study proposes a methodology to be studied in the future to improve different forecasting techniques in real-life applications where there is no need to respond in real time.
Hivatkozás stílusok: IEEEACMAPAChicagoHarvardCSLMásolásNyomtatás
2024-03-02 04:27