Automatic segmentation of parallel drainage patterns supported by a graph convolution neural network

Drainage pattern (DP) recognition is critical in hydrographic analysis, topography identification, and drainage characteristic detection. The traditional method is based on rule computation and self-similarity idea prelimi-narily performing the DP classification. However, DP segmentation is an uncertain spatial cognitive problem affected by enormous factors. To settle such a multi-conditions decision question, this study takes the segmen-tation of parallel drainage pattern (SPDP) as an example presenting a deep learning method, namely the graph convolution neural network (GCNN) based on Graph SAmple and aggreGatE (GraphSAGE). First, a directed graph and dual graph were used to construct a dual drainage graph recording spatial-cognition features of drainage. Second, nine drainage features were built to define the graph description from three perspectives: topological connectivity, meandering equilibrium, and directional unity. Finally, the GraphSAGE model was designed for SPDP and trained by typical samples to finish the segmentation works. The experiment examined the optimal feature combination and hyperparameter sensitivity, which can provide sufficient information for SPDP supported by GraphSAGE. Besides, our model outperformed other machine learning methods and GCNNs driven by a fixed quantity sampling mechanism and hydrological knowledge. This work provides a vital refer-ence for hydrology research supported by combing hydrological knowledge with GCNNs.

Automated summary

In this study, a deep learning method based on graph convolution neural network (GCNN) is proposed for the segmentation of parallel drainage pattern (SPDP). By constructing a dual drainage graph and defining drainage features, the segmentation task is accomplished. The experiment demonstrates that the proposed method outperforms other machine learning methods and GCNNs, providing a crucial reference for hydrology research.