Uncertainty and sensitivity analysis techniques for hydrologic modeling.
Uncertainty and sensitivity analysis techniques for hydrologic modeling.
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DOI：
10.2166/hydro.2009.048
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发表时间：
2009-07
影响因子：
2.7
通讯作者：
Srikanta Mishra
中科院分区：
文献类型：
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作者：
Srikanta Mishra
正式的不确定性和敏感性分析技术使水文建模人员能够量化可能结果的范围，每个结果的可能性以及对输出不确定性的关键因素的评估。这些信息是对标准确定性点估计的改进，用于在不确定性下做出工程决策。本文概述了各种不确定性分析技术，允许模型输入的不确定性映射到模型预测的不确定性。这些方法包括蒙特卡罗模拟法、一阶二阶矩分析法、点估计法、逻辑树分析法和一阶可靠度法。还介绍了灵敏度分析技术的概述，允许识别那些控制模型预测的不确定性的参数。这些方法包括逐步回归、互信息（熵）分析和分类树分析。两个案例研究，以证明这些技术的实用性。本文还讨论了进行不确定性和敏感性分析的系统框架。
Formal uncertainty and sensitivity analysis techniques enable hydrologic modelers to quantify the range of likely outcomes, likelihood of each outcome and an assessment of key contributors to output uncertainty. Such information is an improvement over standard deterministic point estimates for making engineering decisions under uncertainty. This paper provides an overview of various uncertainty analysis techniques that permit mapping model input uncertainty into uncertainty in model predictions. These include Monte Carlo simulation, first-order second-moment analysis, point estimate method, logic tree analysis and first-order reliability method. Also presented is an overview of sensitivity analysis techniques that permit identification of those parameters that control the uncertainty in model predictions. These include stepwise regression, mutual information (entropy) analysis and classification tree analysis. Two case studies are presented to demonstrate the practical applicability of these techniques. The paper also discusses a systematic framework for carrying out uncertainty and sensitivity analyses.