Abstract
Air pollution forecasting, particularly of PM2.5 levels, can be used not only to deliver effective warning information to the public but also to provide support for decisions regarding the control and treatment of air pollution problems. However, there are still some challenging issues in air pollution forecasting that urgently need to be solved, such as how to handle and model outliers, improve forecasting stability, and correct forecasting results. In this context, this study proposes an outlier-robust forecasting system to attempt to tackle the abovementioned issues and bridge the gap in the current research. Specifically, the system developed consists of two parts that deal with point and interval forecasting, respectively. For point forecasting, a data preprocessing module is proposed based on outlier handling and data decomposition to mitigate the negative influences of outliers and noise, which can also help the model capture the main characteristics of the original time series. Meanwhile, an outlier-robust forecasting module is designed for better modeling of the preprocessed data. For the model to further improve its accuracy, a nonlinear correction module based on an error ensemble strategy is developed that can provide more accurate forecasting results. Finally, the interval forecasting part of the system is based on a newly proposed artificial intelligence–based distribution evaluation and the results of the point forecasting part to present the range of future changes. Experimental results and analysis utilizing daily PM2.5 concentration from two provincial capital cities in China are discussed to verify the superiority and effectiveness of the system developed, which can be considered an effective technique for point and interval forecasting of daily PM2.5 concentration.
Highlights
Urbanization, industrialization, and energy consumption have caused the issue of air pollution to become increasingly serious
According to the abovementioned analysis and discussion, we can reasonably conclude that the system developed can be a promising tool for daily PM2.5 concentration interval forecasting
The nonlinear correction module is developed based on an error ensemble strategy, which can mine information in the forecasting results and further improve the model’s forecasting performance
Summary
Urbanization, industrialization, and energy consumption have caused the issue of air pollution to become increasingly serious. As far as we are aware, most previous studies have employed data decomposition to improve forecasting performance while ignoring the significance of handling and modeling outliers in air pollution data, which may lead to the hybrid model being unable to further enhance the forecasting performance. Most previous studies have emphasized the contribution of advanced data decomposition and optimization algorithms while ignoring the significance of mining the characteristics of the original air pollution time series and correcting forecasting results to further improve the model’s forecasting performance, despite the growing importance of air pollution forecasting performance Another issue with air pollution forecasting, especially daily air pollution forecasting, is that it is mainly focused on point forecasting and can only provide deterministic information that is insufficient for real application and cannot provide uncertainty information. The methods are presented in the Methodology section, the construction of the outlier-robust point and interval forecasting system are discussed, the Experimental Analysis section presents the experiments, and the final section draws the conclusions of this study
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