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Parameterization and uncertainty analysis in modeling: An application to soil greenhouse gas emission models.

机译：建模中的参数化和不确定性分析：在土壤温室气体排放模型中的应用。

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摘要

The aims of the dissertation were to understand and simulate soil greenhouse gas (GHG) emissions as a decision-support tool using both the systematic- and process-based models and to develop uncertainty and identifiability analysis method for model parameterization and evaluation. The proposed systematic method, a two-parameter gamma-distribution-based unit response curve (UR) model, has potential to be used to estimate the extra GHG emissions from manure and fertilizer applications. The Bayesian inference and the Markov Chain Monte Carlo (MCMC) method were combined to evaluate uncertainties in model parameters and structure and tested on a process-based model (SoilGHG) developed in this study. Results showed that nearly all the posterior parameter ranges from the multivariate normal proposal distribution (MND) of the Metropolis-Hastings algorithm were reduced to be within an order of magnitude. In addition, the covariance matrix of parameters for MND can be estimated from the parameter samples using a univariate distribution; and it is more effective to generate a Markov Chain by updating a single parameter rather than to update the entire parameter vector each time. The uncertainty analysis also generates a small posterior parameter space, which can contribute to the identification of model parameters. The covariance-inverse (CI) was adapted to derive the Hessian matrix via the inverse of the covariance matrix where the condition number of the Hessian is used for the diagnosis of model identifiability (i.e., adequate model performance determined by unique parameter values). Compared with the widely-used difference quotients (DQ) and quasi-analytical (QA) methods, CI is more effective and reliable. The identifiability diagnosis on the SoilGHG model using the CI method implied that the full model was poorly identified, but a reduced model with fewer parameters was "conditionally identifiable". Parameter optimization using the Shuffled Complex Evolution at University of Arizona (SCE-UA) algorithm implied the existence of equifinality (i.e., adequate model performance corresponds to different parameter values) in the model and proved to be effective in reducing the equifinality by attaining most of the parameters with low variations. In conclusion, the proposed uncertainty analysis and identifiability diagnosis methods can provide guidance to model development, parameterization, and evaluation.
机译：本文的目的是利用系统模型和过程模型来理解和模拟土壤温室气体的排放作为决策支持工具，并开发用于模型参数化和评估的不确定性和可识别性分析方法。所提出的系统方法，即基于两参数伽马分布的单位响应曲线（UR）模型，有潜力用于估算粪肥和化肥施用产生的额外温室气体排放量。贝叶斯推理和马尔可夫链蒙特卡洛（MCMC）方法相结合，以评估模型参数和结构的不确定性，并在本研究开发的基于过程的模型（SoilGHG）中进行了测试。结果表明，Metropolis-Hastings算法的多元正态分布（MND）中的几乎所有后验参数范围都减小了一个数量级。此外，可以使用单变量分布从参数样本中估算MND参数的协方差矩阵；并且通过更新单个参数而不是每次都更新整个参数向量来生成马尔可夫链更为有效。不确定性分析还会生成较小的后验参数空间，这可能有助于识别模型参数。协方差逆（CI）用于通过协方差矩阵的逆来推导Hessian矩阵，其中Hessian的条件编号用于模型可识别性的诊断（即由唯一参数值确定的足够的模型性能）。与广泛使用的商（DQ）和准分析（QA）方法相比，CI更有效，更可靠。使用CI方法对SoilGHG模型进行的可识别性诊断表明，完整模型的识别较差，而参数较少的简化模型可以“有条件地识别”。使用亚利桑那大学的混洗复杂演化（SCE-UA）算法进行参数优化意味着模型中存在均等性（即，适当的模型性能对应于不同的参数值），并被证明可以有效地降低均等性低变异的参数。总之，所提出的不确定性分析和可识别性诊断方法可以为模型开发，参数化和评估提供指导。

著录项

  • 作者

    Wang, Gangsheng.;

  • 作者单位

    Washington State University.;

  • 授予单位 Washington State University.;
  • 学科 Engineering Agricultural.;Engineering Environmental.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 215 p.
  • 总页数 215
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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