摘要
为描述掺气减蚀工程中的空腔积水水深、空腔长度与诸多影响因素的非线性关系,实现空腔积水水深和空腔长度的准确计算,在搜集162组试验数据的基础上,建立了支持向量机回归(GS-SVR)模型。通过网格搜索和交叉验证方法,研究了GS-SVR模型中超参数(C和γ)的相互关系和其对GS-SVR模型预测准确度的影响机制,分析了6种不同输入组合(共12组)的模型性能。结果表明:通过网格搜索可以找到模型性能最佳的区域,该区域中C和γ呈现相反的增长趋势;基于上述方法找到了最佳变量组合(i_(1),i_(2),Fr,Δ/h),并且实现了对掺气坎空腔长度和空腔积水水深的精准预测。
In order to describe the nonlinear relationship between the cavity backwater depth,cavity length and many influencing factors in aeration erosion reduction projects,and to achieve the accurate calculation of backwater and cavity length,a support vector machine regression model(GS-SVR)by means of machine learning was established based on the collection of 162 sets of model experimental data.Through the method of grid search and cross-validation,the relationship between the hyperparameters(C and γ)in the support vector machine model and its influence mechanism on the accuracy of the model’s prediction were studied.On this basis,the model performance of six different input combinations(a total of twelve groups)was analyzed.The results show that the region with the best model performance can be found through grid search.In this region,C andγshow the opposite growth trend.Based on the above method,the optimal variable combination can be found(i_(1),i_(2),Fr,Δ/h)and the accurate prediction of the cavity length and the backwater depth is realized.
作者
郭港归
李国栋
魏杰
王立杰
GUO Ganggui;LI Guodong;WEI Jie;WANG Lijie(State key Laboratory of Eco-hydraulics in Northwest Arid Region of China,Xi’an 710048,China;POWERCHINA Zhongnan Engineering Corporation Limited,Changsha 410014,China)
出处
《水利水电科技进展》
CSCD
北大核心
2022年第6期105-110,共6页
Advances in Science and Technology of Water Resources
基金
国家自然科学基金(51579206)。
关键词
空腔积水水深
空腔长度
网格搜索
支持向量机
超参数
cavity backwater depth
cavity length
grid search
support vector machine
hyperparameters
