A combined model based on feature selection and support vector machine for PM2.5 prediction
A combined model based on feature selection and support vector machine for PM2.5 prediction
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基于特征选择和支持向量机的PM2.5预测组合模型
DOI：
10.3233/jifs-202812
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发表时间：
2021
影响因子：
2
通讯作者：
Ying Pan
中科院分区：
文献类型：
--
作者：
Xiaocong Lai;Hua Li;Ying Pan
随着对环境和空气质量的关注度不断提高，PM2.5受到了越来越多的关注。人们期望从气象数据中挖掘有用信息来预测空气污染，然而，空气质量在很大程度上受到……（句子不完整）
With the increasing attention to the environment and air quality, PM2.5 has been paid more and more attention. It is expected to excavate useful information in meteorological data to predict air pollution, however, the air quality is greatly affected by meteorological factors, and how to establish an effective air quality prediction model has always been a problem that people urgently need to solve. This paper proposed a combined model based on feature selection and Support Vector Machine (SVM) for PM2.5 prediction. Firstly, aiming at the influence of meteorological factors on PM2.5, a feature selection method based on linear causality is proposed to find out the causality between features and select the features with strong causality, so as to remove the redundant features in air pollution data and reduce the workload of data analysis. Then, a method based on SVM is proposed to analyze and solve the nonlinear problems in the data, for reducing the prediction error, a method of particle swarm optimization is also used to optimize SVM parameters. Finally, the above methods are combined into a prediction model, which is suitable for the current air pollution control. 12 representative data sets on the UCI (University of California, Irvine) website are used to verify the combined model, and the experimental results show that the model is feasible and effective.
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