基于HHO-SVM的水质预测模型及应用

Water Quality Prediction Model Based on HHO-SVM and Its Application

支持向量机(SVM)在小样本模拟预测中具有优势,但其惩罚因子C和核函数参数γ的选取存在主观性,因此采用哈里斯鹰算法(HHO)对SVM的C、γ进行优化,建立基于HHO-SVM的水质预测模型,并应用于滇池草海西苑隧道断面的水质预测研究中。结果表明,HHO-SVM水质预测模型的预测精度高于基于遗传算法的支持向量机模型(GA-SVM)和基于鲸鱼算法的支持向量机模型(WOA-SVM),验证了HHO算法优化SVM参数的可行性,表明HHO-SVM可用于水质预测中。

Support Vector Machine(SVM)has advantages in small sample simulation prediction,but there is subjectivity in the selection of penalty factor C and kernel function parameterγin SVM.Therefore,the Harris Hawks Optimization(HHO)algorithm was used to optimize C andγin the SVM.And then the HHO-SVM mode was established to predict water quality in the Xiyuan tunnel section of Lake Dianchi Caohai.The results show that the prediction accuracy of the water quality prediction model based on HHO-SVM is higher than that of the SVM based on genetic algorithm(GA-SVM)and the SVM based on whale optimization algorithm(WOA-SVM).It is proved that the HHO is feasible to optimize the parameters in SVM,and HHO-SVM can be used in water quality prediction.