Convolution Recurrent Neural Network for Daily Forecast of PM10 Concentrations in Brunei Darussalam

Abstract

PM10 is a particulate matter with an aerodynamic diameter less than or equal to 10. It is one of the primary pollutants contributing to the ambient air quality level. Air quality monitoring in Brunei Darussalam is using only the PM10 concentrations to measure the nation's daily Pollutant Standard Index (PSI). This study sheds light on a data centric landscape of air pollution prediction in Brunei Darussalam, highlights potential uses of forecasting daily PM10concentrations, and presents comparisons of prediction models built using several methods, namely: moving average, linear regression, recurrent neural network (RNN), long short term memory (LSTM), LSTM with 1D convolutions, and convolutional recurrent neural network (CRNN). This study is using daily PM10 concentrations obtained from the air quality monitoring stations located at every district in Brunei Darussalam for a period of 15 y (2005-2019).