Mapping homogeneous regions for flash floods using machine learning: A case study in Jiangxi province, China
Ruojing Zhang, Yuehong Chen, Xiaoxiang Zhang, Qiang Ma, Liliang Ren
Abstract
Regionalization of flash floods aims to partition a geographical space into homogeneous regions in which flash floods have similar generation mechanism. In this paper, we present a flash flood regionalization approach using machine learning algorithms to generate the flash flood regionalization map. First, the random forest algorithm is used to identify thirteen key factors of flash floods from a series of rainfall and topographic factors that have great potential to drive the occurrence of flash floods. Second, the two-stage hybrid self-organizing-map-based clustering algorithm is built to delineate the homogeneous regions of flash floods according to the identified thirteen key factors. Third, the best clustering result is selected by the clustering validity indices and it is regarded as the initial regionalization map. Last, the post-processing is implemented to obtain the final regionalization map of flash floods. A case study in the Jiangxi province of China was conducted to generate the flash flood regionalization map with eighteen homogeneous regions. The regionalization map clearly divides the historical flash flood events with different densities into different regions and it is visually satisfied. The quantitative evaluation method further confirmed that the flash flood regionalization map can provide the determinant power of 77.31% for the spatial distribution of historical flash flood events. Hence, it is a valuable option to perform the flash flood regionalization of watersheds and it is beneficial for local government in the Jiangxi province of China in future flash flood mitigation and prevention.