Ensemble Machine Learning, Deep Learning, and Time Series Forecasting: Improving Prediction Accuracy for Hourly Concentrations of Ambient Air Pollutants

Abstract This study aims to improve the generalisation capabilities of machine learning models for modelling hourly air pollutant concentrations in scenarios where access to high-quality data is limited. A diverse set of techniques was implemented to tackle this challenge, encompassing the utilisati...

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Bibliographic Details
Main Authors:	Valentino Petrić, Hussain Hussain, Kristina Časni, Milana Vuckovic, Andreas Schopper, Željka Ujević Andrijić, Simonas Kecorius, Leizel Madueno, Roman Kern, Mario Lovrić
Format:	Article
Language:	English
Published:	Springer 2024-09-01
Series:	Aerosol and Air Quality Research
Subjects:	Prophet Ozone Air pollution LSTM CNN Random forests
Online Access:	https://doi.org/10.4209/aaqr.230317
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Ensemble Machine Learning, Deep Learning, and Time Series Forecasting: Improving Prediction Accuracy for Hourly Concentrations of Ambient Air Pollutants

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