Improved grey clustering method in risk zonation of mountain flash flood disaster

Abstract— Flash floods are considered one of the worst weather-related natural disasters. Flash floods are dangerous because they are sudden and highly unpredictable. Identification of the locations of high-risk areas has a major effect on the improvement of flash flood disaster control and prevention. Earlier work conducted on flood disaster risk zonation was commonly based on Digital Elevation Mode (DEM) data and statistical yearbook data and used an index, such as rainfall, topography, slope, or river distribution, with the analytic hierarchy process (AHP) method to determine the weighting. In this method, the final regional risk map was created by using ArcGIS map algebra superposition. In the present study, an improved gray clustering method is put forward to improve the comprehensive evaluation of the risk of mountain flash flood disasters by constructing the exponential whitening function and by using the information entropy weight method, which produces results that are more accurate and more reliable than those of the traditional method. This improved method can make full use of the limited information available, improving not only the resolution but also the influence of the subjective method, and produces more objective and accurate evaluation results. We obtain the risk degree by combining the information entropy weight and improved whitening function approaches in a gray clustering methodology. Additionally, a method is applied to develop models for mapping the risk grade in zones of 1436 towns and counties in Hubei Province with remotely sensed (RS) data and the ArcGIS platform. The results show that the improved approach is useful for rapidly assessing flash flood hazard and vulnerability and for completing risk assessments in mountain areas.