测绘学报 ›› 2019, Vol. 48 ›› Issue (8): 975-984.doi: 10.11947/j.AGCS.2019.20180370

• 地图学与地理信息 • 上一篇    下一篇

案例支撑下的朴素贝叶斯树状河系自动分级方法

段佩祥, 钱海忠, 何海威, 谢丽敏, 罗登瀚   

  1. 信息工程大学地理空间信息学院, 河南 郑州 450000
  • 收稿日期:2018-08-13 修回日期:2019-04-11 出版日期:2019-08-20 发布日期:2019-08-27
  • 通讯作者: 钱海忠 E-mail:haizhongqian@163.com
  • 作者简介:段佩祥(1995-),男,硕士生,研究方向为地图自动综合、空间数据挖掘。E-mail:1515461929@qq.com
  • 基金资助:
    国家自然科学基金(41571442;41171305)

Naive Bayes-based automatic classification method of tree-like river network supported by cases

DUAN Peixiang, QIAN Haizhong, HE Haiwei, XIE Limin, LUO Denghan   

  1. Institute of Geographical Spatial, Information Engineering University, Zhengzhou 450000, China
  • Received:2018-08-13 Revised:2019-04-11 Online:2019-08-20 Published:2019-08-27
  • Supported by:
    The National Natural Science Foundation of China (Nos. 41571442;41171305)

摘要: 河流分级是树状水系综合的关键。现有方法大多根据河段的局部几何特征进行主支流识别,较少顾及河流和河系的整体结构特征,且使用多指标综合评价判别时对权重的设定缺乏科学的方法,对综合知识利用较少,应用的灵活性有待提高。对此,本文从案例学习的角度出发,针对河段主支流关系识别,提出一种基于朴素贝叶斯的树状河系自动分级方法。首先,从已有成果数据中提取出主支流分类的案例,利用朴素贝叶斯机器学习方法进行训练得到主支流分类模型;对于待分类树状河系,使用分类模型,从河口出发自下游向上游依次计算各上游河段分类为主流的概率,以概率最大的上游河段作为主流河段,将各主流河段依次连接得到主流河流;主流河流以外的支流部分,重复以上步骤进行层次结构化实现河系分级。试验证明,本文方法能很好地模仿专家意图,对树状河系的主支流进行很好地识别分类,并构建合理的层次结构,分级效果良好。

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

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