Novel hybrid extreme learning machine and multi-objective optimization algorithm for air pollution prediction.

• A novel hybrid system is proposed with deterministic prediction and interval prediction. • Outlier detection and correction method are used to reduce the complexity of the series. • Data decomposition strategy is applied to decompose the original series to improve the prediction accuracy. • A multi-objective optimization algorithm is selected to determine the original weights and biases of the forecasting model. • Applied interval prediction to quantify the range of concentration change of air pollutants caused by uncertainties. A novel system regarding deterministic and interval predictions of pollutant concentration is constructed in this study, which can not only obtain higher prediction accuracy in deterministic prediction and also provide effective interval prediction of air pollutant concentration. In the deterministic prediction stage, the improved extreme learning machine combines outlier detection and correction algorithm, data decomposition strategy, and a multi-objective optimization algorithm to form a hybrid model for predicting pollutant concentration. Moreover, the applicability of the optimization algorithm was verified from theoretical and experimental analysis. In the interval prediction stage, three distributions are compared to mine, the traits of deterministic prediction errors are analyzed, and interval prediction is designed to quantify the uncertainties associated with pollutant concentration. To investigate the prediction performance of the proposed system, comparison experiments have been executed using the PM 2.5 concentration series from three cities. The results indicate that the system proposed in this paper outperforms comparison models in forecasting accuracy and has advantages for pollution prediction. [ABSTRACT FROM AUTHOR]