基于Copula熵理论的大坝渗流统计模型因子优选

Input factor optimization study of dam seepage statistical model based on copula entropy theory

针对大坝渗流统计模型需要考虑较多的前期项,造成参选的因子数量较大,进而导致常规方法的建模误差较高的问题,研究引入Copula熵理论,利用Copula熵和偏互信息(partial mutual information,PMI)相结合的方法,对输入因子的选取进行优化。针对Copula熵的求取,Copula函数采用Gumbel函数,分布采用柯西分布代替正态分布,并引入Hample准则来精确选取因子。将该方法在糯扎渡大坝渗流监测中进行应用,并与常规的因子选择方法进行对比分析,结果表明,采用基于Copula熵的因子优化选取方法的渗流统计模型具有更好的预测效果。

In order to avoid the conventional method＇s requirement of selecting large quantities of input factors as well as its large errors in development of a dam seepage statistical model,problems caused by the need for many items to be considered in the earlier stage of model development,the copula entropy theory combined with partial mutual information was used to optimize the input factor selection. To obtain the copula entropy,the Gumbel function was used as the copula function,the Cauchy distribution was used to replace the normal distribution,and the Hample criterion was used to select the input factors accurately. This approach was applied to seepage detection for the Nuozhadu Dam. Comparison of the present results with those obtained from the conventional factor selection approach shows that the seepage statistical model for optimizing input factor selection based on the copula entropy has a better prediction effect.