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 air pollutants being SO₂, NO₂ and PM₁₀ 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 averages of RMSE of ESN and MLP for SO₂ are 5.9 ppb and 6.9 ppb, respectively. For PM₁₀, the accuracy of ESN is 83.8% with MAE of 53.5 μg/m³, while the accuracy of MLP is only 77.6% with MAE of 68.2 μg/m³. For NO₂, 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 air forecasting model.

Keywords: Forecast, air quality, ESN, MLP, ANN, Hanoi, Vietnam.