The Mathematical Model Of Guangxi Flood Disaster Based On Copula Function

Nowadays, the problems of climate warming and environmental deterioration have become a widespread concern and need to be solved. As a common and extreme natural disaster, floods particularly serious harm to humans. Because of the special subtropical monsoon climate and unique karst landforms of Guangxi, floods is more frequent. Taking into account the source and damage of floods are response by a number of multiple variables, so Copula function is used to build return period models of the source and damage. And through the comparative analysis, we can get the occurrence of floods in Guangxi.Firs of all, according to the actual situation of Guangxi’s floods, the re-search index variables of the floods’source and disaster are selected. By using moment method, probability weighted moment method and maximum likelihood estimation method, we estimate the parameters of the distribution that conform to the floods’ indicator variables. And find the appropriate indexes’ distribution using testing goodness of fit.Secondly, based on the research of the precipitation data of Guangxi, the joint distribution of the mean annual precipitation and annual precipitation extremum average is set up according to the theory of Copula function. With the goodness-of-fit evaluation analysis, the Gumbel-Hougaard Copula function is found that it can well describe the joint distribution. And the design values that calculate from two variables joint return period will be more safe as the flood control standard.Finally, the joint distribution of the affected population, the affected area of farm crops and direct economic losses is established using copulas function theory.The Gumbel-Hougaard Copula function is the best by the test of goodness of fit. Through calculating the return period of flood disaster, the frequency of some degree of flood disaster can be predicted. The prediction model that based on copulas function can better reflect the damage of the occurrence of flood disaster, when compare with GM (1,1) disaster prediction model.