Dynamic System Approach for Improved PM2.5 Prediction in Taiwan

This study presents two dynamic models, namely recurrent neural network and long short-term memory (LSTM) models, for predicting PM2.5 concentrations in Taiwan by using PM2.5 time series obtained at air quality monitoring stations and weather information obtained at neighboring weather stations. The proposed models can efficiently predict PM2.5 by incorporating a learned memory structure with a forgetting gate. To evaluate the predictive performance of the proposed models, large-scale databases established by Taiwan's Environmental Protection Administration, and Central Weather Bureau were used; these databases include hourly data from 77 air quality monitoring stations and 580 weather stations over a 1-year period. The results demonstrated that the proposed models outperformed three traditional machine learning methods (gradient boosting, support vector machine, and classification and regression tree models) by 27.12% and 33.69% on average in terms of the coefficient of determination and root mean square error, respectively. A geographical divergence analysis was conducted to compare predictive performance in different regions. The results revealed that the most significant improvement in predictive performance was achieved in central Taiwan. The seasonal and pollution effect on predictive performance were reduced by the LSTM and the source distribution of PM2.5 emission in Taiwan was also analyzed.