Flood Hazard Analysis Based on Copula Connect Function

The study adopts the Copula function to evaluate index data and historical flood disaster simulation samples to reduce the subjectivity of the evaluation results. A genetic algorithm is used to calculate the model parameters and predict flood hazard levels. The spatial data processing technology of the geographic information system (GIS) is employed to extract and analyze spatial data to acquire indicators. A comprehensive hazard evaluation index system containing a maximum of 1, 6, 24 h heavy rain, relative height difference, average gradient, and drainage density is established to perform detailed analysis. The complex links of the evaluation index values to flood hazard analysis are uncovered by applying this data-focused flood hazard evaluation strategy. By comparing the actual occurrence times and forecast results of flood disasters in 64 research areas of Hubei Province, we find the established model has good prediction effect and can provide data support for flood disaster early warning.