RT journal article
T1 Hourly pollutants forecasting using a deep learning approach to obtain the AQI
A1 Moscoso López, José Antonio
A1 González Enrique, Francisco Javier
A1 Urda, Daniel
A1 Ruiz Águilar, Juan Jesús
A1 Turias Domínguez, Ignacio José
A2 Ingeniería Industrial e Ingeniería Civil
A2 Ingeniería Informática
K1 exogenous variables
K1 feature ranking
K1 artificial neural networks
K1 LSTM
K1 deep learning
K1 machine learning
K1 AQI
K1 air pollution forecasting
AB The Air Quality Index (AQI) shows the state of air pollution in a unique and more understandable way. This work aims to forecast the AQI in Algeciras (Spain) 8 hours in advance. The AQI is calculated indirectly through the predicted concentrations of five pollutants (O3 , NO2 , CO, SO2 and PM10 ) to achieve this goal. Artificial neural networks (ANNs), sequence-to-sequence long short-term memory networks (LSTMs) and a newly proposed method combing a rolling window with the latter (LSTMNA ) are employed as the forecasting techniques. Besides, two input approaches are evaluated: using only the data from the own time series of the pollutant in the first case or adding exogenous variables in the second one. Severalwindow sizes are employed (24, 28 and 72 hours) with ANNs and LSTM NA s. Additionally, several feature ranking methods are applied in the exogenous approach to select the most relevant lagged variables to feed the models. Results show how the proposed exogenous approach increases the performance of the prediction models. Besides, the newly proposed method LSTMNA provides the best performances in most of the cases evaluated. Hence, it constitutes an exciting alternative to standard LSTMs and ANNs to predict pollutants concentrations and, consequently, the AQI.
PB Oxford University Press
SN 1367-0751
YR 2022
FD 2022
LK http://hdl.handle.net/10498/35206
UL http://hdl.handle.net/10498/35206
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026