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A study on the application of Echo State Network (E SN) for the forecast of air quality in Hanoi for a period of seven days, which is based on the nonlinear relationships between the concentrations of an air pollutant to be forecasted and meteorological parameters, was conducted. Three air pollutants being SO 2, NO2 and PM10 were selected for this study. Training data and testing data were extracted from the datab ase of Lang air quality monitoring station, Hanoi, from 2003 to 2009. Values forecasted by ESN are compared with those by MLP (Multilayer Perception). Results shown that, in alm ost experiments, the performance of ESN is better than that of MLP in terms of the values and the correlation of concentration trends. The average of RMSE of ESN and MLP for SO 2 are 5.9 ppb and 6.9 ppb, respectively. For PM 10, the accuracy of ESN is 83.8 % with MAE of 53.5 μg/m, while the accuracy of MLP is only 77.6 % with MAE of 68.2 μg/m. For NO2, the performance of ESN and MLP is similar; the ac curacy of both models is in the range of 60 % to 72.7 %. Thes e suggest that, ESN is a novel and feasible approach to build the air forecasting model.

NGHIÊN CỨU ỨNG DỤNG MẠNG TRẠNG THÁI PHẢN HỒI ĐỂ DỰ BÁO CHẤT LƯỢNG KHÔNG KHÍ