PM2.5 Concentration Prediction Based on Markov Blanke Feature Selection and Hybrid Kernel Support Vector Regression Optimized by Particle Swarm Optimization

PM2.5 Concentration Prediction Based on Markov Blanke Feature Selection and Hybrid Kernel Support Vector Regression Optimized by Particle Swarm Optimization

Abstract This study employed air quality and meteorological data as research materials and extracted the optimal feature subset by using the approximate Markov blanket-based normal maximum relevance minimum redundancy (nMRMR) algorithm to serve as the input data of the prediction model. In addition,...

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Bibliographic Details
Main Authors: Lian-Hua Zhang, Ze-Hong Deng, Wen-Bo Wang
Format: Article
Language:English
Published: Springer 2021-02-01
Series:Aerosol and Air Quality Research
Subjects:
PM2.5
Maximum relevance minimum redundancy (MRMR)
Hybrid kernel
Support vector regression
Prediction model
Online Access:https://doi.org/10.4209/aaqr.200144
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https://doi.org/10.4209/aaqr.200144

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