检索结果-南通市图书馆

Suspended sediment yield modeling in mahanadi river, India by multi-objective optimization hybridizing artificial intelligence algorithms

引用

International Journal of Sediment Research 2021年第1期36卷 76-91页

作者： Arvind Yadav Snehamoy Chatterjee Sk Md Equeenuddin Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation Department of Mining Engineering National Institute of Technology Department of Geological and Mining Engineering and Sciences Michigan Technological University Department of Earth & Atmospheric Sciences National Institute of Technology

river sediment produced through weathering is one of the principal landscape modification processes on *** are an integral part of the hydrologic cycle and are the major geologic agents that erode the continents and transport water and sediments to the *** of suspended sediment yield is always a key parameter for planning and management of any river *** is always challenging to model sediment yield using traditional mathematical models because they are incapable of handling the complex non-linearity and *** suspended sediment modeling of the river depends on the number of factors such as rock type,relief,rainfall,temperature,water discharge and catchment *** this study,we proposed a hybrid genetic algorithm-based multi-objective optimization with artificial neural network(GA-MOO-ANN) with automated parameter tuning model using these factors to estimate the suspended sediment yield in the entire mahanadi river *** model was validated by comparing statistically with other models,and it appeared that the GA-MOO-ANN model has the lowest root mean squared error(0.009) and highest coefficient of correlation(0.885) values among all comparative models(traditional neural network,multiple linear regression,and sediment rating curve) for all *** was also observed that the proposed model is the least biased(0.001) ***,the proposed GA-MOOANN is the most capable model,compared to other studied models,for estimating the suspended sediment yield in the entire mahanadi river basin,*** results also suggested that the proposed GA-MOO-ANN model is unable to estimate suspended sediment yield satisfactorily at gauge stations having very small catchment areas whereas performing satisfactorily on locations having moderate to the large catchment *** models provide the best result at Tikarapara,the gauge station location in the extreme downstream,having the largest catchment area.

关键词： Artificial neural network Genetic algorithm Multi-objective genetic algorithm Suspended sediment yield mahanadi river

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

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：