基于粒子群优化和支持向量机的花粉浓度预测模型

doi:10.11772/j.issn.1001-9081.2018071626

计算机应用 ›› 2019, Vol. 39 ›› Issue (1): 98-104.DOI: 10.11772/j.issn.1001-9081.2018071626

• 2018年全国开放式分布与并行计算学术年会(DPCS 2018)论文 • 上一篇下一篇

基于粒子群优化和支持向量机的花粉浓度预测模型

赵文芳^1,2, 王京丽¹, 尚敏³, 刘亚楠²

1. 中国气象局北京城市气象研究所, 北京 100089;
2. 北京市气象信息中心, 北京 100089;
3. 玉溪师范学院地理与国土工程学院, 云南玉溪 653100

收稿日期:2018-07-19 修回日期:2018-08-20 发布日期:2019-01-21 出版日期:2019-01-10
通讯作者: 尚敏
作者简介:赵文芳(1980-),女,湖北鄂州人,高级工程师,硕士,主要研究方向:大数据、云计算、机器学习、气象大数据处理;王京丽(1960-),女,北京人,研究员,主要研究方向:大气探测、探测仪器研制、大气环境;尚敏(1979-),女,云南玉溪人,讲师,硕士,主要研究方向:数据挖掘、地理信息应用;刘亚楠(1982-),男,北京人,工程师,主要研究方向:软件架构、网络安全、大数据分析。
基金资助:
国家自然科学基金资助项目（41575156）；中国气象局2019年度气象软科学研究重点项目（19）。

Forecasting model of pollen concentration based on particle swarm optimization and support vector machine

ZHAO Wenfang^1,2, WANG Jingli¹, SHANG Min³, LIU Yanan²

1. Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China;
2. Beijing Meteorological Information Center, Beijing 100089, China;
3. College of Geography and Land Engineering, Yuxi Normal University, Yuxi Yunnan 653100, China

Received:2018-07-19 Revised:2018-08-20 Online:2019-01-21 Published:2019-01-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (41575156), the Special Soft Science Project of China Meteorological Administration (19).

摘要/Abstract

摘要： 为了提高花粉浓度预报的准确率，解决现有花粉浓度预报准确率不高的问题，提出了一种基于粒子群优化（PSO）算法和支持向量机（SVM）的花粉浓度预报模型。首先，综合考虑气温、气温日较差、相对湿度、降水量、风力、日照时数等多种气象要素，选择与花粉浓度相关性较强的气象要素构成特征向量；其次，利用特征向量与花粉浓度数据建立SVM预测模型，并使用PSO算法找出最优参数；然后利用最优参数优化花粉浓度预测模型；最后，使用优化后的模型对花粉未来24 h浓度进行预测，并与未优化的SVM、多元线性回归法（MLR）、反向神经网络（BPNN）作对比。此外使用优化后的模型对某市南郊观象台和密云两个站点进行逐日花粉浓度预测。实验结果表明，相比其他预报方法，所提方法能有效提高花粉浓度未来24 h预测精度，并具有较高的泛化能力。

关键词: 花粉浓度, 支持向量机, 粒子群优化算法, Spark, 花粉浓度预测

Abstract: To improve the accuracy of pollen concentration forecast and resolve low accuracy of current pollen concentration forecast model, a model for daily pollen concentration forecasting based on Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM) was proposed. Firstly, the feature vector extraction was carried out by using correlation analysis technique to select meteorological data with strong correlation with pollen concentration, such as temperature, daily temperature difference, relative humidity, precipitation, wind, sunshine hours. Secondly, an SVM prediction model based on this vector and pollen concentration observation data was established. The PSO algorithm was designed to optimize the parameters in SVM algorithm, and then the optimal parameters were used to construct daily pollen concentration prediction model. Finally, the forecast of pollen concentration in 24 hours in advance was made by using the optimized SVM model. The comparison among the accuracy of the optimized SVM model, Multiple Linear Regression (MLR) model and Back Propagation Neural Network (BPNN) model was performed to evaluate their performances. In addition, the optimized model was also applied for the forecast of pollen concentration in 24 hours in advance at Nanjiao and Miyun meteorological observation stations. The experimental results show that the proposed method performs better than MLR and BPNN methods. Meanwhile, it also provides promising results for forecast of pollen concentration in 24 hours in advance and also has good generalization ability.

Key words: pollen concentration, Support Vector Machine (SVM), Particle Swarm Optimization (PSO) algorithm, Spark, pollen concentration forecast

中图分类号:

TP391
TP181

赵文芳, 王京丽, 尚敏, 刘亚楠. 基于粒子群优化和支持向量机的花粉浓度预测模型[J]. 计算机应用, 2019, 39(1): 98-104.

ZHAO Wenfang, WANG Jingli, SHANG Min, LIU Yanan. Forecasting model of pollen concentration based on particle swarm optimization and support vector machine[J]. Journal of Computer Applications, 2019, 39(1): 98-104.

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基于粒子群优化和支持向量机的花粉浓度预测模型

Forecasting model of pollen concentration based on particle swarm optimization and support vector machine

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