Comparison of Statistical and Deep Learning Methods for Forecasting PM2.5 Concentration in Northern Thailand

Abstract

This study applies statistical methods and deep learning techniques to forecast the daily average PM2.5 concentration in northern Thailand, where the concentration is usually high and exceeds the safe level. The data used in the analysis are collected from January 2018 to December 2020 from 16 air monitoring stations. The statistical methods used are Holt-Winters exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), and dynamic linear model (DLM). The deep learning techniques considered in this study are the recurrent neural network (RNN) and long-short term memory (LSTM). To compare the predictive performance of both methods, we use the root mean square error (RMSE). The result indicates that statistical methods, especially ARIMA, perform better than the deep learning techniques in most stations. Moreover, LSTM tends to provide higher accuracy than the RNN, especially with more number of nodes.