Forecasting Air Quality in Tripoli: An Evaluation of Deep Learning Models for Hourly PM2.5 Surface Mass Concentrations

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Date
2023-02-28
Authors
Esager, Marwa Winis Misbah
Ünlü, Kamil Demirberk
Journal Title
Journal ISSN
Volume Title
Publisher
Atmosphere
Abstract
In this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli.
Description
Open Access Atmosphere 2023, 14(3), 478; https://doi.org/10.3390/atmos14030478 Marwa Winis Misbah Esager, Graduate School of Natural and Applied Sciences, Atilim University, Ankara, Turkey, Kamil Demirberk Ünlü Department of Industrial Engineering, Atilim University, Ankara, Turkey; demirberk.unlu@atilim.edu.tr
Keywords
Neural network modeling, time series analysis, particulate matter, modeling and fore- 25 casting, Libya
Citation
http://hdl.handle.net/20.500.14411/1846