Prediction of Air Pollution Interval Based on Data Preprocessing and Multi-Objective Dragonfly Optimization Algorithm - Wang, Jiyang; Li, Jingrui; Li, Zhiwu (Frontiers in Ecology and Evolution, 2022)
With the rapid development of global industrialization and urbanization, as well as the continuous expansion of the population, large amounts of industrial exhaust gas and automobile exhaust have been released. In order to sound a better early warning of air pollution, researchers have proposed many pollution prediction methods. However, 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 change trend of pollution data and analyze local characteristics, but also provide strong technical support for air pollution early warning.