Drainage pattern recognition method considering local basin shape based on graph neural network

Abstract

Drainage pattern recognition is crucial for geospatial understanding and hydrologic modelling. Currently, drainage pattern recognition methods employ geometric measures of overall and local features of river networks but lack measures of river basin unit shape features, so that potential correlations between river segments are usually ignored, resulting in poor drainage pattern recognition results. In order to overcome this problem, this paper proposes a supervised graph neural network method that considers the local basin unit shape of river networks. First, based on the overall hierarchy of the river networks, the confluence angle of river segments and the shape of river basin units, multiple drainage pattern classification features are extracted. Then, typical drainage pattern samples from the multi-scale NSDI and USGS databases are used to complete the training, validation and testing steps. Experimental results show that the drainage pattern indexes proposed can describe the characteristics of different drainage patterns. The method can effectively sample the adjacent river segments, flexibly transfer the associated pattern features among river segment neighbours, and aggregate the deeper characteristics of the river networks, thus improving the drainage pattern recognition accuracy relative to other methods and reliably distinguishing different drainage patterns.

Keywords

• river
• drainage pattern recognition
• basin unit shape
• supervised learning
• graph neural networks