Quantification of river network types based on hierarchical structures

被引:6
|
作者
Li, Minhui [1 ]
Wu, Baosheng [1 ]
Chen, Yi [1 ]
Li, Dan [2 ]
机构
[1] Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing 10084, Peoples R China
[2] China Coal Res Inst, Beijing 100013, Peoples R China
基金
国家重点研发计划;
关键词
Yellow River; River network pattern; Classification; Hierarchical structure; DRAINAGE NETWORKS; JUNCTION ANGLES; CLASSIFICATION; CONNECTIVITY; EXTRACTION; EFFICIENT; PATTERNS; IMPACTS; BASIN;
D O I
10.1016/j.catena.2021.105986
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The development of river networks is closely related to landscape evolution processes and terrain analysis. Several methods for classifying river networks based on qualitative evaluation or quantitative indices have been developed. However, these methods pay little attention to the hierarchical structures of river networks and omit some generalized and detailed information about basins. In this work, a classification method based on the hierarchical structure of river networks is established. Drainage texture, flow direction and aspect ratios are employed to describe river network characteristics. The specific attributes calculated include the drainage density, the stream frequency of river networks and the maximum frequency, and the distribution uniformity of the flow direction of river reaches on each order. Classification trees are established using these metrics. The method is applied to classify 83 river networks in the Yellow River source zone ranging in size from 17 to 241 km(2). A previous classification method using basin invariance attributes was contrasted with our network structure approach, and cross-validation (CV) accuracies were obtained of 66.8% and 82.0% respectively. To verify the applicability of the proposed method, another 45 river networks classified in previous studies are considered. A CV accuracy of 76.7% is achieved with the proposed method. These results demonstrate that the proposed method based on the hierarchical structures of river networks is more accurate than methods that ignore the hierarchical structure.
引用
收藏
页数:14
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