Daily PM_(2.5) concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm

摘要

Increased attention has been paid to PM_(2.5) pollution in China. Due to its detrimental effects on environment and health, it is important to establish a PM_(2.5) concentration 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 PM_(2.5) concentration. 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 PM_(2.5) concentration prediction. Therefore the established model presents the potential to be applied to air quality forecasting systems.