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PM2.5 has become a serious concern in Thailand, particularly in Bangkok and its vicinity. It has significant impacts on human health, economy and society. A prediction method based on mathematical models is an alternative approach to obtain estimates of PM2.5 concentrations. This research proposes long short-term memory (LSTM) neural networks to develop models to forecast the levels of fine particulate matter in Bangkok, ambient and roadside areas. Correlation analysis was used to select the key variables, and the RMSE and MAPE criteria were employed as forecasting performance measures. Eight different models were constructed using air quality and meteorological data. The results demonstrated that for ambient area, model 2 (which includes the variables PM10, NO2, CO, O3, SO2 and LAGPM2.5) was the best model with an average RMSE of 8.05 and an average MAPE of 27.22. In roadside area, model 8 (which contains the variables PM10, NO2, CO, Temp, Hum, Press, WindSp, WindDir and LAGPM2.5) showed the best performance with an average RMSE of 4.83 and an average MAPE of 22.57. Additionally, the prediction models at roadside site (model 5-8) were more accurate than the others (model 1-4). Estimates based upon short-term past data, 1 day, tended to have smaller forecast errors.
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