文献类型：期刊文献

英文题名：A Multiple Kernel Learning Approach for Air Quality Prediction

作者：Zheng, Hong[1];Li, Haibin[1];Lu, Xingjian[1,2];Ruan, Tong[1]

机构：[1]East China Univ Sci & Technol, Informat Engn & Comp Sci Coll, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Smart City Collaborat Innovat Ctr, Shanghai 200240, Peoples R China

年份：2018

卷号：2018

外文期刊名：ADVANCES IN METEOROLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000436288100001)】；

基金：The authors are pleased to acknowledge the National Natural Science Foundation of China under Grant nos. 61103115 and 61103172; the National Natural Science Youth Foundation of China under Grant no. 61602175; the special fund for Software and Integrated Circuit Industry Development of Shanghai under Grant no. 150809; and the "Action Plan for Innovation on Science and Technology" Projects of Shanghai (Project no. 16511101000).

语种：英文

摘要：Air quality prediction is an important research issue due to the increasing impact of air pollution on the urban environment. However, existing methods often fail to forecast high-polluting air conditions, which is precisely what should be highlighted. In this paper, a novel multiple kernel learning (MKL) model that embodies the characteristics of ensemble learning, kernel learning, and representative learning is proposed to forecast the near future air quality (AQ). The centered alignment approach is used for learning kernels, and a boosting approach is used to determine the proper number of kernels. To demonstrate the performance of the proposed MKL model, its performance is compared to that of classical autoregressive integrated moving average (ARIMA) model; widely used parametric models like random forest (RF) and support vector machine (SVM); popular neural network models like multiple layer perceptron (MLP); and long short-term memory neural network. Datasets acquired from a coastal city Hong Kong and an inland city Beijing are used to train and validate all the models. Experiments show that the MKL model outperforms the other models. Moreover, the MKL model has better forecast ability for high health risk category AQ.