Hourly PM2.5 concentration multi-step forecasting method based on extreme learning machine, boosting algorithm and error correction model.

• A novel multi-step forecasting method of hourly PM 2.5 concentration is proposed. • Two boosting algorithms and two multi-step forecasting strategies are analyzed. • Error correction model is used to correct the prediction error. The multi-step forecasting of PM 2.5 concentration is helpful to realize the early warning of air pollution, but the accurate multi-step forecasting of PM 2.5 has certain difficulties. In this paper, a novel multi-step forecasting method of hourly PM 2.5 concentration is proposed. Two boosting algorithms, Modified AdaBoost.RT and Gradient Boosting, are used to enhance the extreme learning machine (ELM) for ensemble prediction of the PM 2.5. Then two multi-step forecasting strategies, multiple-input multiple-output (MIMO) and recursive, are used. Finally, through error correction model (ECM) the prediction error is corrected to obtain the hourly PM 2.5 multi-step forecasting results. Corresponding experiments are carried out through the PM 2.5 data sets of four cities, and the results show that: (1) the forecasting method proposed in this study can achieve a good multi-step forecasting effect of PM 2.5 , and changing the forecasting strategy or boosting algorithm has little influence on the forecasting effect; (2) the use of ECM can improve the PM 2.5 forecasting accuracy of the model, and as the forecasting steps increase, the improvement effect of ECM is more significant; (3) the forecasting framework proposed in this paper is effective, and the forecasting accuracy of the proposed method is significantly better than the corresponding single models and the existing models. [ABSTRACT FROM AUTHOR]