检索结果-南通市图书馆

Runoff modeling in Ungauged Catchments Using Machine Learning Algorithm-Based model Parameters Regionalization Methodology

引用

Engineering 2023年第9期28卷 93-104页

作者： Houfa Wu Jianyun Zhang Zhenxin Bao Guoqing Wang Wensheng Wang Yanqing Yang Jie Wang State Key Laboratory of Hydraulics and Mountain River Engineering College of Water Resource and HydropowerSichuan UniversityChengdu 610065China State Key Laboratory of Hydrologywater Resources and Hydraulic Engineering Nanjing Hydraulic Research InstituteNanjing 210029China Research Center for Climate Change Ministry of Water ResourcesNanjing 210029China

model parameters estimation is a pivotal issue for runoff modeling in ungauged *** nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization,which is the most widely used *** modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin(YHHRB).The values of the Nash–Sutcliffe efficiency coefficient(NSE),coefficient of determination(R2),and percent bias(PBIAS)indicated the acceptable performance of the soil and water assessment tool(SWAT)model in the *** descriptors belonging to the categories of climate,soil,vegetation,and topography were used to express the catchment characteristics related to the hydrological *** quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models,including linear regression(LR)equations,support vector regression(SVR),random forest(RF),k-nearest neighbor(kNN),decision tree(DT),and radial basis function(RBF).Each of the 38 catchments was assumed to be an ungauged catchment in ***,the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor ***,the similaritybased regionalization scheme was used for comparison with the regression-based *** results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged *** with the traditional LR-based approach,the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear *** performances of different approaches were similar in humid regions,while the advantages of the machine learning techniques were more evident in arid *** the study area contained nested catchments,the best result was calculated with the similarity-based

关键词： Parameters estimation Ungauged catchments Regionalization scheme Machine learning algorithms soil and water assessment tool model

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：