A spatial multi-resolution multi-objective data-driven ensemble model for multi-step air quality index forecasting based on real-time decomposition

Abstract In recent years, the problem of urban air pollution has become more serious. Effective Air Quality Index (AQI) forecasting can provide a reliable guarantee for pollution control and air quality warnings. In this research background, a spatial multi-resolution data-driven ensemble AQI forecasting model based on real-time decomposition is proposed. The model uses spatial correlation analysis to screen out the auxiliary sites that are most relevant to the target site. Wavelet Packet Decomposition (WPD) decomposes the original AQI data into multiple subseries. The iterative refresh mechanism of real-time decomposition makes the decomposition method more valuable in engineering applications. The classic averaging method and deep learning-based Stacked Auto-Encoder (SAE) are used to obtain low-resolution data and high-resolution data, respectively. Subseries containing fluctuation information and depth features are input into Outlier Robust Extreme Learning Machine (ORELM) for forecasting. Multi-Objective Wolf Colony Algorithm (MOWCA) is utilized for the ensemble of multiple resolution predictors. Then, the high-correlation auxiliary sites tested by the Pearson method are input into the model where the target site is located. After the optimized ensemble of the new meta-heuristic bat algorithm, the final AQI forecasting result is produced. To comprehensively evaluate the proposed model, historical air quality data from 26 city monitoring stations in China is used for experimental comparison. Experimental results show that each component of the proposed model has a positive effect on the forecasting performance of the model. The proposed model has excellent forecasting performance, and its performance is superior to other comparative models. Reasonable consideration of multi-resolution and space-time data provides more ideas for the enrichment of the air pollution forecasting model library.