Assessment of Deep Neural Network Models for Direct and Recursive Multi-Step Prediction of PM10 in Southern Spain

Javier Gómez-Gómez (), Eduardo Gutiérrez de Ravé and Francisco J. Jiménez-Hornero
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Javier Gómez-Gómez: GEPENA Research Group, Gregor Mendel Building (3rd Floor), Campus Rabanales, University of Cordoba, 14071 Cordoba, Spain
Eduardo Gutiérrez de Ravé: GEPENA Research Group, Gregor Mendel Building (3rd Floor), Campus Rabanales, University of Cordoba, 14071 Cordoba, Spain
Francisco J. Jiménez-Hornero: GEPENA Research Group, Gregor Mendel Building (3rd Floor), Campus Rabanales, University of Cordoba, 14071 Cordoba, Spain

Forecasting, 2025, vol. 7, issue 1, 1-21

Abstract: Western Europe has been strongly affected in the last decades by Saharan dust incursions, causing a high PM10 concentration and red rain. In this study, dust events and the performance of seven neural network prediction models, including convolutional neural networks (CNN) and recurrent neural networks (RNN), have been analyzed in a PM10 concentration series from a monitoring station in Córdoba, southern Spain. The models were also assessed here for recursive multi-step prediction over different forecast periods in three different situations: background concentration, a strong dust event, and an extreme dust event. A very important increase in the number of dust events has been identified in the last few years. Results show that CNN models outperform the other models in terms of accuracy for direct 24 h prediction (RMSE values between 10.00 and 10.20 μg/m 3 ), whereas the recursive prediction is only suitable for background concentration in the short term (for 2–5-day forecasts). The assessment and improvement of prediction models might help the development of early-warning systems for these events. From the authors’ perspective, the evaluation of trained models beyond the direct multi-step predictions allowed to fill a gap in this research field, which few articles have explored in depth.

Keywords: PM10; Saharan dust; neural networks; convolutional neural networks; recurrent neural networks; recursive prediction; multi-step prediction (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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