基于PCA-SHO-SVM和PCA-SHO-BP模型的径流预测

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。

Runoff prediction based on PCA-SHO-SVM and PCA-SHO-BP models

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.