Prediction of Air Pollution Interval Based on Data Preprocessing and Multi-Objective Dragonfly Optimization Algorithm

Abstract

With the rapid development of global industrialization and urbanization, as well as the continuous expansion of the population, large amounts of industrial exhaust gases and automobile exhaust are released. To better sound an early warning of air pollution, researchers have proposed many pollution prediction methods. However, the traditional point prediction methods cannot effectively analyze the volatility and uncertainty of pollution. To fill this gap, we propose a combined prediction system based on fuzzy granulation, multi-objective dragonfly optimization algorithm and probability interval, which can effectively analyze the volatility and uncertainty of pollution. Experimental results show that the combined prediction system can not only effectively predict the changing trend of pollution data and analyze local characteristics but also provide strong technical support for the early warning of air pollution.