Improvement on PM-10 Forecast by Using Hybrid ARIMAX and Neural Networks Model for the Summer Season in Chiang Mai

Abstract

Since the air monitoring stations do not provide the relation between other toxic gas and meteorological parameters with the particulate matter up to 10 micrometer or PM-10. The influence of meteorological as well as correlation with other toxic gas is investigated and used them to forecast PM-10 in the case of Chiang Mai province of Thailand. In this paper an attempt to develop hybrid models of an Autoregressive Integrated Moving Average (ARIMA) model with other exogenous variables (ARIMAX) and Neural Networks (NNs), the two hybrid models, i.e. hybrid ARIMAX-NNs model and hybrid NNs-ARIMAX model were implemented to forecast PM-10 for highly season during January-April of Chiang Mai Province. Simulation results of hybrid model are compared with the results of ARIMA, ARIMAX and NNs model. The experimental results demonstrated that the hybrid NNs-ARIMAX model outperformed best over the hybrid ARIMAX-NNs model, ARIMAX model, NNs model, and ARIMA model respectively. In this case study and maybe other cases, it has proved that the NNs model should be priori captured and filtered the non-stationary non-linear component while the fully linearly stationary residuals were accurately predicted by ARIMAX model later.