Identifying Homogeneous Regions for Flash Floods Using Graph Clustering Neural Networks in Jiangxi Province, China

Identifying homogeneous flash flood regions through regionalization is essential for effective mitigation and prevention. However, most existing regionalization methods focus primarily on attribute similarity (e.g., meteorological and underlying factors), while ignoring structural similarity that reflects topological network and flow relationships among catchments. In this study, we developed a new graph-clustering-neural-network-based flash flood regionalization (GFFR) method to address these limitations and improve the homogeneous region delineation. Catchments were first represented as a directed graph. Within GFFR, we then designed a graph convolutional autoencoder to learn latent representations that capture both catchment structure and attributes, while a decoder grouped the catchments into clusters. GFFR was applied in Jiangxi province, China, where it outperformed three typical clustering methods. Historical flash flood events were used to validate the GFFR map, presenting strong spatial consistency with dense event clusters and achieving a determinant power of 81%. Furthermore, the GFFR achieved a 24% higher determinant power than the average performance of the three compared methods. Overall, GFFR provides a valuable tool for flash flood regionalization, while the delineated regions offer critical guidance for governmental flash flood prevention and mitigation strategies.