Graph neural network method for the intelligent selection of river system

Abstract

The spatial features and generalisation rules for river network generalisation are difficult to directly quantify using indicators. To consider dimensional information hidden in river networks and improve river network selection accuracy, this study introduces a graph convolutional neural network-based method. First, we modelled the river network as a graph structure, where the nodes represent each river segment and edges represent the connections between river segments. The semantic, geometric, and morphological features of individual river segments and topological and constraint features between river segments were then calculated to characterise the relevant nodes. Second, under supervised classification, the input node attributes and labels were sampled and aggregated to obtain richer and more abstract high-level features. The graph convolutional neural network model then selected or deleted river segments. Finally, the selected individual river segments were connected to obtain the complete integrated river network. A 1:10,000 scale map of the Min River system in the Yangtze River Basin was tested, with a 1:50,000 scale map used as the control, and it yielded a correct classification rate >95%. Moreover, the correct classification rate was 7.35%–5.31% and 7.7%–3.3% higher than that of other graph neural network methods and traditional machine learning methods, respectively.