Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov chain Monte Carlo sampling
Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov chain Monte Carlo sampling
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DOI：
10.1016/j.advwatres.2007.12.003
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发表时间：
2008-04-01
影响因子：
4.7
通讯作者：
Zyvoloski, George A.
中科院分区：
文献类型：
--
作者：
Blasone, Roberta-Serena;Vrugt, Jasper A.;Zyvoloski, George A.
在过去的几十年里，水文学家在利用动态模拟模型分析和理解水文系统方面取得了巨大的进展。然而，这些模型的预测往往是确定性的，因此，它们侧重于最可能的预测，而没有明确估计相关的不确定性。这种不确定性来自于不完整的过程表示，初始条件的不确定性，输入，输出和参数误差。广义似然不确定性估计（GLUE）框架是在蒙特卡洛（MC）分析的背景下，结合贝叶斯估计和不确定性传播来表示预测不确定性的第一次尝试之一。由于它的灵活性，易于实现和分布式计算机系统上的并行实现的适用性，GLUE方法已被用于各种各样的应用。然而，在GLUE中通常使用的先前参数空间的基于MC的采样策略在寻找行为模拟方面不是特别有效。这对于高维参数估计问题以及需要大量计算时间来运行并产生所需输出的复杂仿真模型的情况下变得尤其成问题。在本文中，我们提高了计算效率的GLUE采样先验参数空间使用自适应马尔可夫链蒙特卡罗计划（洗牌复杂进化大都会（SCEM-UA）算法）。此外，我们提出了一种替代策略，以确定截断阈值的值的基础上，得到的不确定性界限的适当覆盖。我们证明了这一修正的GLUE方法与三个不同的概念流域模型的复杂性不断增加，使用合成和现实世界的流量数据从两个集水区不同的水文制度的优越性。(c)2007爱思唯尔有限公司保留所有权利。
In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit estimate of the associated uncertainty. This uncertainty arises from incomplete process representation, uncertainty in initial conditions, input, output and parameter error. The generalized likelihood uncertainty estimation (GLUE) framework was one of the first attempts to represent prediction uncertainty within the context of Monte Carlo (MC) analysis coupled with Bayesian estimation and propagation of uncertainty. Because of its flexibility, ease of implementation and its suitability for parallel implementation on distributed computer systems, the GLUE method has been used in a wide variety of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models that require significant computational time to run and produce the desired output. In this paper we improve the computational efficiency of GLUE by sampling the prior parameter space using an adaptive Markov Chain Monte Carlo scheme (the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm). Moreover, we propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the Superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic and real-world stream-flow data from two catchments with different hydrologic regimes. (c) 2007 Elsevier Ltd. All rights reserved.