检索结果-南通市图书馆

Assessment of gully erosion susceptibility using different DEM-derived topographic factors in the black soil region of Northeast China

引用

International Soil and Water Conservation Research 2023年第1期11卷 97-111页

作者： Donghao Huang Lin Su Lili Zhou Yulu Tian Haoming Fan College of Water Conservancy Shenyang Agricultural UniversityShenyang110866China College of Forestry Shenyang Agricultural UniversityShenyang110866China Key Laboratory of Soil Erosion Control and Ecological Restoration in Liaoning ProvinceShenyang110866China College of Urban and Environmental Sciences Northwest UniversityXi'an710127China

As a primary sediment source,gully erosion leads to severe land degradation and poses a threat to food and ecological ***,identification of susceptible areas is critical to the prevention and control of gully *** study aimed to identify areas prone to gully erosion using four machine learning methods with derived topographic *** topographic attributes(elevation,slope aspect,slope degree,catchment area,plan curvature,profile curvature,stream power index,and topo-graphic wetness index)were derived as feature variables controlling gully occurrence from digital elevation models with four different pixel sizes(5.0 m,12.5 m,20.0 m,and 30.0 m).A gully inventory map of a small agricultural catchment in Heilongjiang,China,was prepared through a combination of field surveys and satellite *** topographic attribute dataset was randomly divided into two portions of 70%and 30%for calibrating and validating four machine learning methods,namely random forest(RF),support vector machines(SVM),artificial neural network(ANN),and generalized linear models(GLM).Accuracy(ACC),area under the receiver operating characteristic curve(AUC),root mean square error(RMSE),and mean absolute error(MAE)were calculated to assess the performance of the four machine learning methods in predicting spatial distribution of gully erosion susceptibility(GES).The results suggested that the selected topographic attributes were capable of predicting GES in the study catchment area.A pixel size of 20.0 m was optimal for all four machine learning *** RF method described the spatial relationship between the feature variables and gully occurrence with the greatest accuracy,as it returned the highest values of ACC(0.917)and AUC(0.905)at a 20.0 m *** RF was also the least sensitive to resolutions,followed by SVM(ACC=0.781-0.891,AUC=0.724-0.861)and ANN(ACC=0.744-0.808,AUC=0.649-0.847).GLM performed poorly in this study(ACC=0.693-0.757,AUC=0.608-0.703).Ba

关键词： Gully erosion Machine learningmethods topographicattribute Pixel size Northeast China

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

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：