A novel regional identification framework integrating clustering and delaunay for urban flood-prone zones

Accurate identification of urban flood-prone zone is of critical importance for enhancing emergency response efficiency and optimizing the allocation of disaster relief resources, particularly in developing countries facing increasingly severe urban flood challenges. Despite the widespread application of clustering algorithms in regional identification, most existing methods generally ignore the rationality of the resulting region boundaries, making it difficult to meet the practical requirements of boundary accuracy in disaster management. Accordingly, this paper proposes a noise-insensitive clustering algorithm based on boundary processing called CDC+, which obtain regional boundary while effectively eliminating potential noise. Furthermore, to further improve the accuracy of region boundary delineation, a planar scanning constraint algorithm based on Delaunay Triangulation (referred to as DSC) is introduced, overcoming the limitation of conventional Delaunay Triangulation, which can only generate convex hull boundaries, thereby enhancing the scientificity of spatial representation. Finally, CDC+ and DSC are integrated to establish a regional identification framework, and applying to a flood-prone zone from Zhejiang Province. Experimental results demonstrate that the proposed method can effectively identify spatially clustered regions with rational boundaries, providing strong support for scientifically managing flood disasters and facilitating the precise allocation of emergency response resources.