Abstract
The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence network is utilized to extract high-level features. eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features, as well as predict haze. Establish PM2.5 concentration pollution grade classification index, and grade the forecast data. The expert experience knowledge is utilized to assist the optimization of the pre-warning results. The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine (SVM) and Back Propagation (BP) widely used at present, the accuracy has greatly improved compared with SVM and BP.
Highlights
Haze is caused by many factors, such as meteorological and non-meteorological factors
In order to improve the detection accuracy of haze early-warning and reduce the harm to human production and life, the paper aims to study the method of haze feature extraction and pollution grade prediction in the Beijing-Tianjin-Hebei region
The traditional numerical and statistical prediction methods are mainly used in haze prediction [Liu, He and Lau (2018); Ma, Shao, Xu et al (2018)]
Summary
Haze is caused by many factors, such as meteorological and non-meteorological factors. In order to improve the detection accuracy of haze early-warning and reduce the harm to human production and life, the paper aims to study the method of haze feature extraction and pollution grade prediction in the Beijing-Tianjin-Hebei region. Haze has the characteristics of complex causes and strong nonlinearity, so it is difficult to obtain satisfactory results by using relatively simple statistical methods to predict the variation its trend [Han, Seo, Kim et al (2019)]. XGBoost is a parallel computing algorithm, which has the advantages such as fast operation speed, good robustness and high prediction accuracy It can better solve the problems of over learning, low prediction efficiency, long training time and only suitable for small samples existed in the above methods [Mishra, Goyal and Upadhyay (2015)].
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