Fazzini, P.; Montuori, M.; Pasini, A.; Cuzzucoli, A.; Crotti, I.; Campana, E.F.; Petracchini, F.; Dobricic, S. Forecasting PM10 Levels Using Machine Learning Models in the Arctic: A Comparative Study. Remote Sens.2023, 15, 3348.
Fazzini, P.; Montuori, M.; Pasini, A.; Cuzzucoli, A.; Crotti, I.; Campana, E.F.; Petracchini, F.; Dobricic, S. Forecasting PM10 Levels Using Machine Learning Models in the Arctic: A Comparative Study. Remote Sens. 2023, 15, 3348.
Fazzini, P.; Montuori, M.; Pasini, A.; Cuzzucoli, A.; Crotti, I.; Campana, E.F.; Petracchini, F.; Dobricic, S. Forecasting PM10 Levels Using Machine Learning Models in the Arctic: A Comparative Study. Remote Sens.2023, 15, 3348.
Fazzini, P.; Montuori, M.; Pasini, A.; Cuzzucoli, A.; Crotti, I.; Campana, E.F.; Petracchini, F.; Dobricic, S. Forecasting PM10 Levels Using Machine Learning Models in the Arctic: A Comparative Study. Remote Sens. 2023, 15, 3348.
Abstract
In this study, we present a statistical forecasting framework and assess its efficacy using a range of established machine learning algorithms for predicting Particulate Matter (PM) concentrations in the Arctic, specifically in Pallas (FI), Reykjavik (IS), and Tromso (NO). Our framework leverages historical ground measures and 24-hour predictions from 9 models provided by the Copernicus Atmosphere Monitoring Service (CAMS) to provide PM predictions for the following 24 hours. Furthermore, we compare the performance of various memory cells based on artificial neural networks (ANN), including recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory networks (LSTMs), echo state networks (ESNs), and windowed multi-layer perceptrons (MLPs), which are commonly employed in time series forecasting tasks. Irrespective of the chosen memory cell type, our results demonstrate that the proposed framework consistently outperforms the CAM models in terms of mean squared error (MSE), with average improvements ranging from 25% to 40%. Additionally, we investigate the impact of outliers on the overall model performance.
Keywords
deep learning; PM10; environmental forecasting; chaotic time series; Arctic
Subject
Environmental and Earth Sciences, Pollution
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.