摘要
Air quality prediction is an important part of environmental governance.The accuracy of the air quality prediction also affects the planning of people’s outdoor activities.How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention,pollution control and pollution early warning.In this paper,we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants.Firstly,the data of air pollutants in different cities are collected by a sliding window technology,and the data of different cities in the sliding window are clustered by Kohonen method to find the same tends in air pollutants.On this basis,combined with the weather data,we use the ReliefF method to extract the characteristics of climate factors that helpful for prediction.Finally,different types of air pollutants and corresponding extracted the characteristics of climate factors are used to train different sub models.The experimental results of different algorithms with different air pollutants show that this method not only improves the accuracy of air quality prediction,but also improves the operation efficiency.
基金
This research was supported in part by the National Natural Science Foundation of China under grant Nos.61602202 and 61603146
the Natural Science Foundation of Jiangsu Province under contracts BK20160428 and BK20160427
the Six talent peaks project in Jiangsu Province under contract XYDXX-034
the project in Jiangsu Association for science and technology.
