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Earth Science Informatics

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22-11-2023 | RESEARCH

Prediction of hourly PM₁₀ concentration through a hybrid deep learning-based method

Authors: Sahar Nasabpour Molaei, Ali Salajegheh, Hassan Khosravi, Amin Nasiri, Abbas Ranjbar Saadat Abadi

Published in: Earth Science Informatics | Issue 1/2024

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Abstract

Air pollution can have detrimental effects on human health as well as the environment. Particulate Matter (PM), as a global issue, is a type of air pollution that consists of small particles suspended in the air. Therefore, it is crucial to estimate and monitor levels of PM in the air in order to protect public health and the environment. This study proposed a novel hybrid method to apply the capability of two various deep learning models, namely, the encoder-decoder convolutional neural network and the Long Short-Term Memory (LSTM) model for PM₁₀ prediction. The first model was utilized as a data argumentation method to enhance dataset diversity, and the LSTM model employed meteorological parameters and spatiotemporal factors to estimate the PM₁₀ levels. The proposed technique achieved performance resulting in a coefficient of determination value of 0.88 and a mean absolute error value of 7.24. The results confirm that the developed hybrid method as an effective tool of PM prediction can be used to inform decision-making about policies and actions to reduce PM levels.

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Title: Prediction of hourly PM10 concentration through a hybrid deep learning-based method
Authors: Sahar Nasabpour Molaei
Ali Salajegheh
Hassan Khosravi
Amin Nasiri
Abbas Ranjbar Saadat Abadi
Publication date: 22-11-2023
Publisher: Springer Berlin Heidelberg
Published in: Earth Science Informatics / Issue 1/2024
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-023-01146-w

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