An automatic river classification and selection method supported by random forest and graph neural network

doi:10.11947/j.AGCS.2025.20240385

Abstract

Abstract:

Nowadays, with the widespread use of deep learning methods in the field of cartography, the use of graph neural networks (GNNs) to solve the generalization problems of unstructured vector map data has become a research hotspot. A river network automatic classificationand selection method supported by random forests and GNN is proposed to address the shortcomings of the existing methods, which mainly start from local structures and do not consider the local correlation of adjacent river segments when applying machine learning methods to classify rivers, and only consider the topological relationship of river segments in the process of river network selection. Firstly, incorporating knowledge of river network classification and local standardization of features, the random forest algorithm is used to automatically classify river network. Then, a dual branch GNN selection model is constructed by integrating topological connections and spatial proximity relationships between river segments, and the selected classification of river segments is achieved through supervised learning. Finally, the river network selection results are obtained by adopting a connectivity preservation strategy that considers hierarchical levels. The experimental results show that compared with standardization ways of Min-Max and Z-Score, the automatic classification accuracy of the river network has been improved by 11.42 percentage points and 12.39 percentage points respectively, and the classification effect is better. Compared with existing river network selection methods based on graph neural networks, the selection accuracy has been improved by 2.2 percentage points, and closer to the labeled data.

Key words: river network selection, automatic classification, random forest, graph neural network, feature local standardization

CLC Number:

P208

Fubing ZHANG, Qun SUN, Qing XU, Jingzhen MA, Wenjun HUANG, Ruoxu CHEN. An automatic river classification and selection method supported by random forest and graph neural network[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(9): 1697-1711.

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选取单元河段数统计	训练集（700）		测试集（71）
选取单元河段数统计	1∶24 000	1∶250 000	1∶24 000	1∶250 000
平均值	627.7	212.2	549.0	191.9
最小值	85.0	37.0	137.0	49.0
最大值	1 684.0	556.0	1 476.0	460.0

河系编号	训练集			河系编号	测试集
河系编号	主流数	支流数	图示	河系编号	主流数	支流数	图示
河系1	133	132		河系6	450	454
河系2	371	372		河系7	550	551
河系3	205	205		河系8	478	480
河系4	391	391		河系9	378	377
河系5	341	341		河系10	160	159

标准化方式	测试集分类准确率	主支流识别准确率
Min-Max标准化	84.42	90.34
Z-Score标准化	83.45	89.05
特征局部标准化	95.84	97.03

标准化方式	特征重要性
标准化方式	RLen	URL	WArea	URA	BAngle	URN
Min-Max标准化	0.066	0.161	0.085	0.145	0.350	0.192
Z-Score标准化	0.072	0.154	0.092	0.134	0.364	0.185
特征局部标准化	0.031	0.325	0.043	0.330	0.142	0.130

机器学习算法	测试集分类准确率	主支流识别准确率
高斯分布朴素贝叶斯	92.62	94.60
决策树	93.88	93.85
随机森林	95.84	97.03