Forecasting PM<sub>10</sub> Levels Using Machine Learning Models in the Arctic: A Comparative Study

Forecasting PM<sub>10</sub> 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
Main Authors: Paolo Fazzini, Marco Montuori, Antonello Pasini, Alice Cuzzucoli, Ilaria Crotti, Emilio Fortunato Campana, Francesco Petracchini, Srdjan Dobricic
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Remote Sensing
Subjects:
deep learning
PM<sub>10</sub>
environmental forecasting
chaotic time series
Arctic
Online Access:https://www.mdpi.com/2072-4292/15/13/3348

Internet

https://www.mdpi.com/2072-4292/15/13/3348

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