This study aims to produce accurate predictions of the NO2 concentrations at a specific
station 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 create
the forecasting models. Additionally, a new prediction method was proposed combining LSTMs
using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two
different strategies were followed regarding the input variables: using NO2
from the station or
employing NO2 and other pollutants data from any station of the network plus meteorological
variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window
sizes. Several feature ranking methods were used to select the top lagged variables and include them
in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The
exogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 to
ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising
results 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% of
the cases.
