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

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。

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.