Hybrid Data Mining Forecasting System Based on Multi-Objective Optimization and Selection Model for Air pollutants

Abstract

The air quality index (AQI) indicates the short-term air quality situation and changing trend of the city, which includes six air pollutants: PM2.5, PM10, CO, NO2, SO2 and O3. Due to the diversity of pollutants and the fluctuation of single pollutant time series, it is a challenging task to find out the main pollutants and establish an accurate forecasting system in a city. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to analyze different air pollutants at length, leading to unsatisfactory results. In this study, a model selection forecasting system is proposed that consists of data mining, data analysis, model selection, and multi-objective optimized modules and effectively solves the problems of air pollutants monitoring. The proposed system employed fuzzy C-means cluster algorithm to analyze 13 original AQI series, and fuzzy comprehensive evaluation is used to find out the main air pollutants in each city. And then multiple artificial neural networks are used to forecast the main air pollutants for each category and find the optimal models. Finally, the modified multi-objective optimization algorithm is used to optimize the parameters of optimal models and model selection to obtain final forecasting values from optimal hybrid models. The experiment results of datasets from 13 cities in the Beijing–Tianjin–Hebei Urban Agglomeration demonstrated that the proposed system can simultaneously obtain efficient and reliable data for air quality monitoring.