LAUSR

The increasingly serious haze problem in China has brought about a growing public awareness of air quality. Precise air quality index (AQI) forecasts play an important role in both controlling air pollution and promoting the sustainable development of human society. However, the randomness, non-stationarity, and irregularity of the AQI series make its classifications very difficult. This paper introduces a time-varying inertia weighting (TVIW) strategy based on a combination of gravitation search algorithm (GSA) and particle swarm optimization (PSO) called the TVIW-PSO-GSA. The TVIW-PSO-GSA is utilized to optimize the penalty parameter $C$ and kernel function parameter $\gamma $ of a support vector machine (SVM) to create a hybrid TVIW-PSO-GSA-SVM algorithm. Twenty-three benchmark functions, five UCI datasets, and an AQI hierarchical classification example are tested to find that the convergence speed and performance of the TVI-PSO-GSA exceed those of other algorithms, and the TVIW-PSO-GSA-SVM algorithm also achieves higher classification accuracy and efficiency than the PSO-GSA-SVM, GSA-SVM, GA-SVM, or PSO-SVM, which indicates that the TVIW-PSO-GSA-SVM reliably and accurately classifies AQI and UCI datasets.