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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)

Abstract:

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.

 

References: 32

                  1. Z. L. Wu, A. X. Liu, et al., “Study on Evaluation of Economic Benefits from Pollen Forecast And Service in Tianjin,” Meteorological Monthly, Vol. 37, No. 5, pp. 626-632, 2011
                  2. Y. R. Bai and X. B. Liu, “Pollen Concentration Forecast,” Meteorological Monthly, Vol. 28, No. 6, pp. 56-58, 2002
                  3. X. B. Liu and Y. R. Bai, “Primary Research Method on Forecasting Pollen Concentration,” Meteorological Science and Technology Supplement, Vol. 12, No. 7, pp. 95-97, 2007
                  4. L. Y. Duan, Y. R. Bai, and Z. L. Wu, “Study of Airborne Pollen Prediction Model,” Meteorological Science and Technology, Vol. 21, No. 4, pp. 37-40, 2008
                  5. Z. L. Wu, G. Z. Wan, and Y. R. Bai, “Random Forests,” Machine Learning, Vol. 35, No. 6, pp. 832-838, 2007
                  6. D. S. Zhang, Y. L. Hai, and T. Feng, “Applied Research on 1-4 Day Pollen Concentration Forecast in Beijing Area,” Meteorological Monthly, Vol. 35, No. 6, pp. 128-132, 2010
                  7. D. S. Zhang, J. X. Xu, Z. L. Zhang, T. Feng, and G. Z. Wan, “Preliminary Study on Predicting Model Of Airborne Pollen,” Meteorological Science and Technology, Vol. 30, No. 6, pp. 822-826, 2010
                  8. Z. L. Zhang, D. S. Zhang, and H. J. Hai, “Study on the Weather Conditions of Pollen Concentration in Beijing City,” Meteorological Science and Technology, Vol. 31, No. 6, pp. 406-408, 2003
                  9. Z. L. Zhang, D. S. Zhang, and H. J. Hai, “Daily Total Pollen and Allergic Pollen Forecasting in August in Beijing,” Meteorological Science and Technology, Vol. 34, No. 6, pp. 724-728, 2006
                  10. G. Sun, S. Li, Y. Cao, et al., “Cervical Cancer Diagnosis based on Random Forest,” International Journal of Performability Engineering, Vol. 13, No. 4, pp. 446-457, 2017
                  11. J. Pan, H. Z. Wang, H. Gao, W. X. Zhao, H. X. Huo, and H. R. Dong, “Paradise Pointer: A Sightseeing Scenes Images Search Engine based on Big Data Processing,” in Proceedings of the International Conference of Young Computer Scientists, Engineers and Educators, pp. 448-452, Harbin, China, January 2015
                  12. G. Sun, H. Dong, D. Li, et al., “NTCA: A High-Performance Network Traffic Classification Architecture,” International Journal of Future Generation Communication & Network, Vol. 6, No. 5, pp. 11-20, 2013
                  13. X. M. Zhou, A. L. Su, G. H. Li, W. Q. Gao, C. H. Lin, S. D. Zhu, et al., “Big Data Storage and Parallel Analysis of Grid Equipment Monitoring System,” International Journal of Performability Engineering, Vol. 14, No. 2, pp. 202-209, 2018
                  14. J. F. Wu, W. Y. Qu, Z. Y. Li, and C. Q. Ji, “A Novel Image Retrieval Method with Saliency Feature Vector,” International Journal of Performability Engineering, Vol. 14, No. 2, pp. 223-231, 2018
                  15. W. Zhang and J. J. Chen, “Relief Feature Selection and Parameter Optimization For Support Vector Machine based on Mixed Kernel Function,” International Journal of Performability Engineering, Vol. 14, No. 2, pp. 280-289, 2018
                  16. R. Qian, Y. Wu, X. Duan, G. Q. Kong, and H. Y. Long, “SVM Multi-Classification Optimization Research based on Multi-Chromosome Genetic Algorithm,” International Journal of Performability Engineering, Vol. 14, No. 4, pp. 631-638, 2018
                  17. L. Li, L. Ma, and J. F. He, “PM2.5 Concentration Prediction Model of Least Squares Support Sector Machine based on Feature Vector,” Journal of Computer Applications, Vol. 34, No. 8, pp. 2212-2216, 2014
                  18. J. Liu, P. Yang, W. S. Lv, and J. X. Liu, “Prediction Model of PM2. 5 Mass Concentrations based on Fuzzy Time Series And Support Vector Machine,” Journal of University of Science and Technology Beijing, Vol. 36, No. 12, pp. 1694-1703, 2014
                  19. P. J. Yue, L. L. Zhang, and Y. J. Ma, “Prediction for SO2 Concentration based on the Fuzzy Time Series and Support Vector Machine (SVM) on Expressway,” Journal of Computer System Applications, Vol. 26, No. 6, pp. 1-8, 2017
                  20. L. Chen, D. M. Wu, and Q. Chen, “Prediction of Air Pollution based on Wavelet Analysis and Support Vector Machine,” Journal of Xian University of Science and Technology, Vol. 30, No. 6, pp. 726-730, 2010
                  21. P. G. Oguntunde, G. Lischeid, and O. Dietrich, “Relationship Between Rice Yield and Climate Variables in Southwest Nigeria Using Multiple Linear Regression and Support Vector Machine Analysis,” International Journal of Biometeorology, Vol. 62, No. 3, pp. 459-469, 2018
                  22. X. L. Ji, S. Xu, R. A. Dahlgren, and M. H. Zhang, “Prediction of Dissolved Oxygen Concentration in Hypoxic River Systems Using Support Vector Machine: A Case Study Of Wen-Rui Tang River, China,” Environmental Science and Pollution Research, Vol. 24, No. 19, pp. 16062-16076, 2017
                  23. M. Liu and J. Lu, “Support Vector Machine - An Alternative to Artificial Neuron Network for Water Quality Forecasting in an Agricultural Nonpoint Source Polluted River?” Environmental Science and Pollution Research, Vol. 21, No. 18, pp. 11036-11053, 2014
                  24. C. Bellinger, M. S. M. Jabbar, O. Zaïane, and A. Osornio-Vargas, “A Systematic Review of Data Mining and Machine Learning for Air Pollution Epidemiology,” BMC Public Health, Vol. 17, pp. 907, 2017
                  25. F. Kang, J. S. Li, and J. J. Li, “System Reliability Analysis of Slopes Using Least Squares Support Vector Machines with Particle Swarm Optimization,” Neurocomputing, Vol. 209, No. 15, pp. 46-56, 2016
                  26. Y. L. Wu, Q. He, and T. W. Xu, “Application of Improved Adaptive Particle Swarm Optimization Algorithm in WSN Coverage Optimization,” Chinese Journal of Sensors and Actuators, Vol. 29, No. 4, pp. 559-565, 2016
                  27. X. L. Zhang, W. Chen, B. J. Wang, and X. F. Chen, “Intelligent Fault Diagnosis of Rotating Machinery Using Support Vector Machine with Ant Colony Algorithm For Synchronous Feature Selection and Parameter Optimization,” Neurocomputing, Vol. 167, No. 16, pp. 260-279, 2015
                  28. X. L. Zhang, X. F. Chen, and Z. J. He, “An ACO-Based Algorithm for Parameter Optimization of Support Vector Machines,” Expert Systems with Applications, Vol. 37, No. 9, pp. 6618-6628, 2010
                  29. C. B. Liu, Q. F. Wang, and F. Pan, “Parameters Selection and Stimulation of Support Vector Machines based on Ant Colony Optimization Algorithm,” Center South University (Science and Technology), Vol. 39, No. 6, pp. 1309-1314, 2008
                  30. A. Rakotomamonjy, R. Le Riche, D. Gualandris, and Z. Harchaoui, “A Comparison of Statistical Learning Approaches for Engine Torque Estimation,” Control Engineering Practice, Vol. 16, No. 1, pp. 43-45, 2008
                  31. R. Chen, D. Y. Sun, and D. T. Qin, “A Novel Engine Identification Model based on Support Vector Machine and Analysis of Precision-Influencing Factors,” Journal of Central South University (Science and Technology), Vol. 41, No. 4, pp. 1391-1396, 2010
                  32. Z. Y. You, W. R. Chen, and G. J. He, “Adaptive Weight Particle Swarm Optimization Algorithm with Constriction Factor,” in Proceedings of the 2010 International Conference of Information Science and Management Engineering, pp. 245-248, Washington DC, USA, August 2010

                                   

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