摘要
为提高径流预测精度,研究主成分分析(PCA)、斑鬣狗优化(SHO)算法与支持向量机(SVM)、BP神经网络相融合的预测方法。在样本数据筛选上选取PCA方法进行数据降维,使数据样本简洁且更具代表性。利用SHO算法优化SVM关键参数及BP神经网络权阈值,分别提出PCA-SHO-SVM、PCA-SHO-BP径流量预测模型,并与SHO-SVM、PCA-SVM、SVM和SHO-BP、PCA-BP、BP模型的预测结果作对比,通过云南省龙潭水文站年径流量及枯水期月径流量预测为例进行验证。结果表明,PCA-SHO-SVM、PCA-SHO-BP模型对实例年径流量预测的平均相对误差分别为2.34%、2.50%,对月径流量预测的平均相对误差分别为6.15%、6.08%,预测精度均优于其他6种模型,具有较高的预测精度和更强的泛化能力。
To improve the accuracy of runoff prediction,the prediction method that combines principal component analysis(PCA),spotted hyena optimizer(SHO)algorithm,support vector machine(SVM),and BP neural network were studied.The PCA method was selected for data dimensionality reduction in sample data screening to make the data sample concise and more representative.Then SHO algorithm was used to optimize SVM key parameters and BP neural network weight threshold respectively,and the corresponding runoff prediction model of PCA-SHO-SVM and PCA-SHO-BP were proposed accordingly.Furthermore,SHO-SVM,PCA-SVM,SVM and SHO-BP,PCA-BP,BP models were constructed to compare with these two models,and the prediction of annual runoff and monthly runoff in the dry season of Longtan Station in Yunnan Province were used for verification.The results show that the average relative error of PCA-SHO-SVM and PCA-SHO-BP models is 2.34%and 2.50%for the annual runoff prediction of this station,and 6.15%and 6.08%for the monthly runoff prediction,respectively.The prediction accuracy of both models are better than the other 6 models,with higher prediction accuracy and stronger generalization ability.
作者
李代华
LI Daihua(Wenshan Branch,Yunnan Province Hydrology and Water Resources Bureau,Wenshan 663000,China)
出处
《水资源与水工程学报》
CSCD
北大核心
2021年第1期97-102,共6页
Journal of Water Resources and Water Engineering
关键词
径流预测
主成分分析
斑鬣狗优化算法
支持向量机
BP神经网络
数据降维
参数优化
runoff prediction
principal component analysis(PCA)
spotted hyena optimizer(SHO)algorithm
support vector machine(SVM)
BP neural network
data dimensionality reduction
parameter optimization