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.

随着政府和公众对空气质量的日益关注，准确预测空气质量指数对早期指导污染控制和保护公众健康至关重要。这项研究的主要目的是开发用于预测空气污染物的最佳组合预测。因此，提出了一种基于合作信息准则的选择方法，从多个个体预测模型中确定最优预测组。如果该预测与包含其他预测的子组产生的组合不是上乘组合，则该方法将从组中排除单个预测。以合肥市的日常空气质量数据为例，验证了所提方法的有效性。在实证研究的第一阶段，确定了影响合肥市空气质量的四种主要空气污染物。然后分别从十个可用模型中选择四种主要污染物的不同最佳预测组。实验结果表明，来自所选最佳组的单个模型的组合优于最佳单个模型和所有可用模型的组合。最后，我们可以通过综合四种主要空气污染物提供的信息来获得总体空气质量指数的预测值。实验结果表明，来自所选最佳组的单个模型的组合优于最佳单个模型和所有可用模型的组合。最后，我们可以通过综合四种主要空气污染物提供的信息来获得总体空气质量指数的预测值。实验结果表明，来自所选最佳组的单个模型的组合优于最佳单个模型和所有可用模型的组合。最后，我们可以通过综合四种主要空气污染物提供的信息来获得总体空气质量指数的预测值。