关键词: 树状河系, 自动分级, 主支流识别, 案例学习, 朴素贝叶斯

Abstract: River classification is the key to the generalization of tree-like river network. Most of the existing methods mainly identify the main and tributary according to the local geometric characteristics of the reach, and less consider the overall structural characteristics of the river and river network. The weight setting in the use of multi-index comprehensive evaluation lacks of scientific methods, with less utilization of generalization knowledge, and the flexibility of the application needs to be improved. Focusing on these problems, from the perspective of case-based studying, this paper proposes an automatic classification method of tree-like river network based on naive bayes for the identification of main and tributary of reaches. Firstly, the case of the main tributary classification is extracted from the existing data, and the main-tributary classification model is trained by using the naive bayes method. For the new tree-like river network to be classified, starting from the estuary, from the downstream to the upstream the classification model is used to calculate the probability that each upstream section in the intersection is classified as the mainstream. The upstream section with the highest probability is taken as the mainstream section, and the mainstream sections are connected to the mainstream rivers in turn. The above steps are repeated for the tributaries to carry out the hierarchical structuring process to achieve river classification. The experiment proves that this method can imitate the expert's intention well, and the main and tributary of the tree-like river network are well identified, and a reasonable hierarchical structure is constructed. The classification effect is good.

Key words: tree-like river network, automatic classification, main-tributary identification, case-based studying, naive Bayes