Application of echo state network for the forecast of air quality

Abstract

A study on the application of Echo State Network (ESN) 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 an pollutants being SO2, NO2 and PMio were selected for this study. Training data and testing data were extracted from the database 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 almost 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 andMLP for SO2 are 5,9 ppb and 6.9 ppb, respectively. For PMio, the accuracy of ESN is 83.8 % with MAE of 53.5 pg/m3, while the accuracy of MLP is only 77.6 % with MAE of 68.2 pg/m3, ForN02, the performance of ESN and MLP is similar; the accuracy of both models is in the range of 60 % to 72.7 %. These suggest that, ESN is a novel and feasible approach to build the an forecasting model.