RT info:eu-repo/semantics/article
T1 Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)
A1 González Enrique, Francisco Javier
A1 Ruiz Aguilar, Juan Jesús
A1 Moscoso López, José Antonio
A1 Urda Muñoz, Daniel
A1 Deka, Lipika
A1 Turias Domínguez, Ignacio J.
K1 Forecasting
K1 Feature selection
K1 Air pollution
K1 Nitrogen dioxide
K1 Artificial neural networks
K1 LSTMs
K1 Exogenous variables
K1 Deep learning
K1 Time series
K1 Informática
K1 Computer science
AB This study aims to produce accurate predictions of the NO2 concentrations at a specificstation of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks(ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to createthe forecasting models. Additionally, a new prediction method was proposed combining LSTMsusing a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Twodifferent strategies were followed regarding the input variables: using NO2from the station oremploying NO2 and other pollutants data from any station of the network plus meteorologicalvariables. The ANN and LSTM-CVT exogenous models used lagged datasets of different windowsizes. Several feature ranking methods were used to select the top lagged variables and include themin the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. Theexogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 toρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promisingresults as the best performing models per prediction horizon employed this new methodology.Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% ofthe cases.
PB MDPI
YR 2021
FD 2021-03
LK http://hdl.handle.net/10259/7233
UL http://hdl.handle.net/10259/7233
LA eng
NO This research was funded by MICINN (Ministerio de Ciencia e Innovación-Spain), grant number RTI2018-098160-B-I00, and grant “Ayuda para Estancias en Centros de Investigación del Programa de Fomento e Impulso de la actividad Investigadora de la Universidad de Cádiz”.
DS Repositorio Institucional de la Universidad de Burgos
RD 02-may-2024