检索结果-南通市图书馆

Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale

Journal of Mountain Science 2023年第10期20卷 2975-2992页

作者： Mohammad Sajjad GHAVAMI Shamsollah AYOUBI Mohammad Reza MOSADDEGHI salman naimi Department of Soil Science College of AgricultureIsfahan University of TechnologyIsfahan ***Iran

Knowledge about the spatial distribution of the soil physical and mechanical properties is crucial for soil management,water yield,and sustainability at the watershed scale;however,the lack of soil data hinders the application of this tool,thus urging the need to estimate soil properties and consequently,to perform the spatial *** research attempted to examine the proficiency of three machine learning methods(RF:Random Forest;Cubist:Regression Tree;and SVM:Support Vector Machine)to predict soil physical and mechanical properties,saturated hydraulic conductivity(Ks),Cohesion measured by fall-cone at the saturated(Psat)and dry(Pdry)states,hardness index(HI)and dry shear strength(SS)by integrating environmental variables and soil features in the Zayandeh-Rood dam watershed,central *** determine the best combination of input variables,three scenarios were examined as follows:scenarioⅠ,terrain attributes derivative from a digital elevation model(DEM)+remotely sensed data;scenarioⅡ,covariates of scenarioⅠ+selected climatic data and some thematic maps;scenarioⅢ,covariates in scenarioⅡ+intrinsic soil properties(Clay,Silt,Sand,bulk density(BD),soil organic matter(SOM),calcium carbonate equivalent(CCE),mean weight diameter(MWD)and geometric weight diameter(GWD)).The results showed that for Ks,Psat Pdry and SS,the best performance was found by the RF model in the third scenario,with R2=0.53,0.32,0.31 and 0.41,respectively,while for soil hardness index(HI),Cubist model in the third scenario with R2=0.25 showed the highest *** predicting Ks and Psat,soil characteristics(*** and soil SOM and BD),and land use were the most important *** predicting Pdry,HI,and SS,some topographical characteristics(Valley depth,catchment area,mltiresolution of ridge top flatness index),and some soil characteristics(***,SOM and MWD)were the most important input *** results of this research present moderate accuracy,however,the methodology em

关键词： Machine learning Soil physical property Soilmechanical property Saturatedhydraulic conductivity Soil cohesion Soil shear strength.

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：