title: Daily PM2.5concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm.
abstract: Increased attention has been paid to PM2.5pollution in China. Due to its detrimental effects on environment and health, it is important to establish a PM2.5concentration forecasting model with high precision for its monitoring and controlling. This paper presents a novel hybrid model based on principal component analysis (PCA) and least squares support vector machine (LSSVM) optimized by cuckoo search (CS). First PCA is adopted to extract original features and reduce dimension for input selection. Then LSSVM is applied to predict the daily PM2.5concentration. The parameters in LSSVM are fine-tuned by CS to improve its generalization. An experiment study reveals that the proposed approach outperforms a single LSSVM model with default parameters and a general regression neural network (GRNN) model in PM2.5concentration prediction. Therefore the established model presents the potential to be applied to air quality forecasting systems.
authors.affiliation: Department of Business Administration, North China Electric Power University, Baoding 071000, China. Electronic address: bdsunwei@126.com.
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
authors.affiliation: Department of Business Administration, North China Electric Power University, Baoding 071000, China. Electronic address: sunjingyi0224@126.com.
authors.forename: Jingyi
authors.initials: J
authors.lastname: Sun
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D025341:Principal Component Analysis
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: CS; Concentration forecasting; LSSVM; PCA; PM(2.5)