Prediction of Multivariate Air Quality Time Series Data using Long Short-Term Memory Network
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Penerbit UTM Press
Abstract
In this study, the air quality model based on the Long Short-Term Memory Network (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA) was developed. The prediction of the particulate matter 10 micrometers or less in diameter (PM10) in Malaysia could be made from both models, and their performance was compared. The purpose of comparison between the two models was to determine the most suitable model to use in predicting PM10 since it is the dominant pollutant in Malaysia most of the time, especially during the haze period. This study used air quality data obtained from the Department of Environment Malaysia from July 2017 to June 2019.
Description
Keywords
Air quality, Long Short Memory Network (LSTM), Auto-Regressive Integrated Moving Average (ARIMA), forecasting model, multivariate
Citation
Bakar, M. A. A., Ariff, N. M., Nadzir, M. S. M., Wen, O. L., & Suris, F. N. A. (2022). Prediction of multivariate air quality time series data using long short-term memory network. Malaysian Journal of Fundamental and Applied Sciences, 18(1), 52-59.