Sensitivity analysis of the probability distribution of groundwater level series based on information entropy
Sensitivity analysis of the probability distribution of groundwater level series based on information entropy
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DOI：
10.1007/s00477-012-0556-2
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发表时间：
2012-01
影响因子：
4.2
通讯作者：
X. Zeng;Dong Wang;Jichun Wu
中科院分区：
文献类型：
--
作者：
X. Zeng;Dong Wang;Jichun Wu
信息熵是分析各种过程中不确定性的有效方法。最大熵原理为概率密度函数的参数估计提供了指导。互熵分析是描述非线性和复杂的多变量关系的有效方法。模型输出的概率分布是模型不确定性分析的要素。本文建立了一个综合地下水流场，生成地下水位序列（GLS）。采用基于POME和卡方检验的频率分析方法，得到了GLS的概率分布。采用逐步回归分析和互熵分析等敏感性分析方法，对影响GLS概率密度函数参数的重要不确定性因素进行了评估。分析结果表明，大多数GLS服从正态分布（或对数正态分布），少数服从其它分布。地下水模型的输入变量对正态GLS的均值和方差有不同的影响。互熵分析比逐步回归分析更适合描述非线性、非单调的多变量关系。
Information entropy is an effective method to analyze uncertainty in various processes. The principle of maximum entropy (POME) provides a guide line for the parameter estimation of probability density function (PDF). Mutual entropy analysis is well qualified for delineating the nonlinear and complex multivariable relationship. The probability distribution of model output is the element of model uncertainty analysis. In this paper, a synthetic groundwater flow field is build to produce groundwater level series (GLS). The probability distribution of GLS is obtained by the frequency analysis method based on POME and Chi-Squared test. The important uncertainty factors that affect the parameters of PDF of GLS are assessed by the sensitivity analysis methods, which include stepwise regression analysis and mutual entropy analysis. Results of this analysis indicate that most of the GLS follow normal distribution (or log-normal distribution), while a few obey others. The mean and variance of normal GLS are affected differently by the input variables of groundwater model. Mutual entropy analysis is more competitive and appropriate for delineating the nonlinear and nonmonotonic multivariable relationship than stepwise regression analysis.