RT conference output
T1 Air Pollution forecasting using Long Short-Term Memory Networks in the Bay of Algeciras (Spain)
A1 Rodríguez García, María Inmaculada
A1 Carrasco García, María Gema
A1 González Enrique, Francisco Javier
A1 Ruiz Águilar, Juan Jesús
A1 Turias Domínguez, Ignacio José
A2 Ingeniería Industrial e Ingeniería Civil
A2 Ingeniería Informática
K1 Air Pollution
K1 ANNs
K1 forecasting
K1 Port-city
K1 Shipping emissions
K1 SO2 NO2 and PM10
K1 Sustainability
AB Continuing with similar studies developed in the Bay of Algeciras, a new method from deep learning has been used. Long Short-Term Memory (LSTMs) are applied to predict future concentrations of SO2, NO2and PM10in the port of Algeciras (Spain). Maritime data from the port of Algeciras are kindly provided by the Algeciras Bay Port Authority and meteorological and pollution data are hourly collected in twenty-one monitoring stations at different points of the Bay of Algeciras from 1st January 2017 to 31stDecember 2019 provided by Andalusian Regional Government. The structure of an LSTM consists of a deep net with feedback connections that can process entire sequences of data which makes it better to forecast relevant pieces of data in the sequence and preserve it for several instants of time due to it can therefore have both short-term (like basic Recurrent Networks) and long-term memory. In this work, LSTM models have been used to predict future values of SO2, NO2, and PM10. A cross-validation strategy was adopted to obtain generalization results for unseen patterns. Furthermore, a Bayesian regularization procedure for hyperparameter searching was applied to avoid overfitting. The results have been very promising, obtaining R values above 0.75 for all pollutants in the 2h-ahead predictions as well as above 0.6 in the 8h-ahead forecasts. These results have been more accurately obtained than using shallow artificial neural networks as authors used in previous studies.
PB Elsevier
SN 2352-1465
YR 2023
FD 2023
LK http://hdl.handle.net/10498/32804
UL http://hdl.handle.net/10498/32804
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026