Forecasting PM10 Levels Using Machine Learning Models in the Arctic: A Comparative Study

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 hi...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-07, Vol.15 (13), p.3348
Main Authors: Fazzini, Paolo, Montuori, Marco, Pasini, Antonello, Cuzzucoli, Alice, Crotti, Ilaria, Campana, Emilio Fortunato, Petracchini, Francesco, Dobricic, Srdjan
Format: Article
Language:English
Subjects:
Air pollution
Algorithms
Arctic
Artificial neural networks
Atmospheric models
Atmospheric monitoring
chaotic time series
Comparative studies
deep learning
Emissions
environmental forecasting
Forecasting
Learning algorithms
Long short-term memory
Machine learning
Memory cells
Multilayer perceptrons
Neural networks
Outdoor air quality
Outliers (statistics)
Particulate emissions
Particulate matter
Performance evaluation
PM10
Recurrent neural networks
Remote sensing
Time series
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Summary: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 measurements and 24 h predictions from nine models by the Copernicus Atmosphere Monitoring Service (CAMS) to provide PM10 predictions for the following 24 h. 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 multilayer perceptrons (MLPs). Regardless of the type of memory cell chosen, our results consistently show that the proposed framework outperforms the CAMS models in terms of mean squared error (MSE), with average improvements ranging from 25% to 40%. Furthermore, we examine the impact of outliers on the overall performance of the model.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15133348