Prediction of Multivariate Air Quality Time Series Data using Long Short-Term Memory Network

Abstract

Malaysia often suffers from haze problems almost every year. Therefore, there is a need for good air quality forecasting model for monitoring and management purposes. 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 micrometres 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. The results showed that forecasting for PM10 using multivariate LSTM model was better than the univariate LSTM model and univariate ARIMA model with the lowest root mean square error (RMSE) for those selected stations. The model with a lower RMSE value means better models and provide higher accuracy in forecasting for PM10.