A novel hybrid model for air quality prediction via dimension reduction and error correction techniques

The monitoring of air pollution through the air quality index (AQI) is a fundamental tool in ensuring public health protection. Accurate prediction of air quality is necessary for the timely implementation of measures to control and manage air pollution, thereby mitigating its detrimental impact on human health. A novel hybrid prediction model is proposed, which is EMD-KMC-EC-SSA-VMD-LSTM. Raw AQI index data are decomposed into intrinsic mode functions (IMFs) by empirical mode decomposition (EMD) method. Subsequently, sample entropy (SE) is utilized to assess the intricacy of IMFs, and K-means clustering (KMC) is used to reconstruct them into joint intrinsic mode functions (Co-IMFs). Then, the variational mode decomposition (VMD) is used to transform the complex Co-IMF0 into simpler IMFs. Long short-term memory (LSTM), optimized either by the Sparrow Search Algorithm (SSA), is applied to forecast all IMFs, generating the first prediction sequence. To further refine the forecasting, an error correction (EC) technique is adopted. The error sequence is obtained by subtracting the forecasting sequence from the raw sequence, which is then decomposed by EMD-SSA-VMD. Subsequently, SSA-LSTM is engaged to forecast the decomposed error sequence, generating the error forecasting sequence. Finally, the forecast outcomes are combined with the error predictions to generate the final AQI prediction sequence. The proposed approach undergoes validation across four urban centers and undergoes comparison against a set of eight prediction models. Experimental findings underscore the heightened precision of this hybrid forecasting model in predicting AQI metrics.