A combined model based on feature selection and support vector machine for PM2.5 prediction

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.

Automated summary

This paper proposes a combined model based on feature selection and Support Vector Machine (SVM) for PM2.5 prediction. The model incorporates feature selection to identify causal relationships between meteorological factors and PM2.5, and uses SVM to address nonlinearities in the data, with particle swarm optimization used to optimize parameters. The combined model is verified on 12 datasets from the UCI website, showing feasibility and effectiveness in predicting PM2.5 levels.