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

doi:10.23940/ijpe.18.11.p27.28082819

Abstract

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.

Key words: pollen concentration, support vector regression, particle swarm optimization algorithm, Spark, pollen concentration forecast

Wenfang Zhao, Jingli Wang, Dongchang Yu, and Ge Zhang. Prediction of Daily Pollen Concentration using Support Vector Machine and Particle Swarm Optimization Algorithm [J]. Int J Performability Eng, 2018, 14(11): 2808-2819.

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References

[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,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,Y. R. Bai, “Random Forests,” Machine Learning, Vol. 35, No. 6, pp. 832-838, 2007
[6] D. S. Zhang, Y. L. Hai,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,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,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,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,H. R. Dong, “Paradise Pointer: A Sightseeing Scenes Images Search Engine based on Big Data Processing,” inProceedings 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,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,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,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,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,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,Q. Chen, “Prediction of Air Pollution based on Wavelet Analysis and Support Vector Machine,” Journal of Xi’an University of Science and Technology, Vol. 30, No. 6, pp. 726-730, 2010
[21] P. G. Oguntunde, G. Lischeid,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,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,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,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,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,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,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,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,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,G. J. He, “Adaptive Weight Particle Swarm Optimization Algorithm with Constriction Factor,” inProceedings of the 2010 International Conference of Information Science and Management Engineering, pp. 245-248, Washington DC, USA, August 2010