Identification of drainage patterns using a graph convolutional neural network

Various geological factors shape drainage patterns. Identifying drainage patterns is a classic problem in topographical knowledge mining and map generalization. Existing rule-based methods rely heavily on the parameter settings of cartographers for drainage-pattern recognition. These methods effectively identify drainage patterns in specific areas but require manual parameter tuning to identify drainage patterns in other areas. Owing to the complexity of topological and geometric characteristics, drainage pattern recognition involves nonlinear problems, and it is difficult to build mapping relationships between characteristics and patterns using rule-based methods. Therefore, we proposed a data-driven method based on a graph convolutional neural network to avoid heavy reliance on human experience and automatically mine implicit relationships between characteristics and drainage patterns. First, six typical drainage patterns (dendritic, rectangular, parallel, trellis, reticulate, and fanned) were listed based on map specifications, and the unique characteristics of each drainage pattern were illustrated. Subsequently, the drainage graphs were constructed. The characteristics of the whole, local, and individual units in the drainage networks were quantified based on drainage vector data. Finally, an identification model was developed using graph convolution, self-attention pooling, and multiple fully connected layers for drainage pattern recognition. After training and testing, the accuracy of our model (0.801 ± 0.014) was better than that of the rule-based method (0.572 ± 0.000) and the traditional machine learning methods (less than 0.733 ± 0.016). The results demonstrate that the ability of our model to identify drainage patterns surpasses that of other methods.