Optimal group selection algorithm in air quality index forecasting via cooperative information criterion

Abstract

With the increasing concern of government and the public on air quality, accurately forecasting air quality index is important in guiding pollution control and protecting public health at an early stage. The main purpose of this research is to develop optimal combined forecast for predicting air pollutant. Therefore, a selection method based on cooperative information criterion is proposed to determine the optimal forecasting group from multiple individual forecasting models. The method excludes an individual forecast from a group if the combination produced by the forecast and the subgroup containing other forecasts is not a superior combination. The daily air quality data collected from Hefei are utilized as a case to verify the effectiveness of proposed selection approach. In the first stage of empirical research, four major air pollutants which influence Hefei’s air quality significantly are identified. Then different optimal forecasting groups for four major pollutants are selected from ten available models respectively. The experimental results demonstrate that the combination of individual models from the selected optimal group outperforms both the best individual model and the combination of all available models. Finally, we can obtain the forecasting values of overall air quality index by integrating the information provided by four major air pollutants.