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Prediction of Daily Pollen Concentration using Support Vector Machine and Particle Swarm Optimization Algorithm

Volume 14, Number 11, November 2018, pp. 2808-2819
DOI: 10.23940/ijpe.18.11.p27.28082819

Wenfang Zhaoa,b, Jingli Wanga, Dongchang Yub, and Ge Zhangc

aInstitute of Urban Meteorology, China Meteorological Administration, Beijing, 100089, China
bBeijing Meteorological Information Center, Beijing Meteorological Bureau, Beijing, 100089, China
cBeijing Space Eye Innovation Technology Company, Beijing, 100089, China

(Submitted on August 6, 2018; Revised on September 2, 2018; Accepted on October 21, 2018)


In this paper, a support vector regression model for daily pollen concentration forecasting combined with the particle swarm optimization algorithm was proposed. Firstly, feature vector extraction was carried out by using the correlation analysis technique from meteorological data such as temperature, wind, relative humidity, precipitation, sunshine hours, and atmospheric pressure. Secondly, a support vector regression prediction model based on these vectors and pollen concentration observation data were established. Based on the Spark framework, a parallel particle swarm optimization algorithm was designed to optimize the parameters in the support vector regression algorithm, and then the optimal parameters were used to construct the daily pollen concentration prediction model. Finally, daily prediction of pollen concentration was made by using the optimized support vector regression model. The comparison among the accuracy of this optimized support vector regression model, the multiple linear regression (MLR) model, and the back propagation neural network (BPNN) model is performed to evaluate their performance. The results show that the proposed support vector regression model performs better than the MLR and BPNN models. Meanwhile, they also indicate that SVM provides promising results for prediction of daily pollen concentration.


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