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运用区域灵敏度分析对三卤甲烷模拟的集成供水管网模型进行结构验证。

Structure validation of an integrated waterworks model for trihalomethanes simulation by applying regional sensitivity analysis.

机构信息

Department of Environmental Science and Engineering, Tsinghua University, Beijing, China.

出版信息

Sci Total Environ. 2010 Mar 15;408(8):1992-2001. doi: 10.1016/j.scitotenv.2010.01.037. Epub 2010 Feb 13.

DOI:10.1016/j.scitotenv.2010.01.037
PMID:20156637
Abstract

A previously developed integrated waterworks model for trihalomethanes simulation was validated by applying a new framework for comprehensive model structure validation. The proposed framework followed the procedure of regional sensitivity analysis and also involved correlation analysis and frequency distribution analysis. Through such an analysis framework, a deep insight into model structure and parameters could be gained besides the traditional focuses on model validation, i.e. model performance and rationality of model parameters. The integrated waterworks model, to which the framework was applied, was proved to give good predictions of the simulated variables, and the identified values of model parameters coincided well with the reported values in the literature. The model was also found to have a large proportion of sensitive parameters, no distinct correlations among parameters, and thus a balanced structure. Moreover, most of the sensitive parameters could be well identified and the associated uncertainties significantly reduced. A detailed investigation into the sensitivity, identifiability, and uncertainty of model parameters showed that the model conceptualization was in good agreement with accepted scientific principles and anticipated system behaviors. All these results, therefore, supported the validity and trustworthiness of the model. In addition, further opportunities for refining the model were also proposed.

摘要

先前开发的用于三卤甲烷模拟的综合水厂模型通过应用新的综合模型结构验证框架进行了验证。所提出的框架遵循区域灵敏度分析的程序,还涉及相关分析和频率分布分析。通过这样的分析框架,可以深入了解模型结构和参数,除了传统的模型验证重点,即模型性能和模型参数的合理性。应用该框架的综合水厂模型被证明可以很好地预测模拟变量,并且识别出的模型参数值与文献中报道的值很好地吻合。该模型还被发现具有大量敏感参数,参数之间没有明显的相关性,因此具有平衡的结构。此外,大多数敏感参数可以很好地识别,相关的不确定性也显著降低。对模型参数的敏感性、可识别性和不确定性的详细研究表明,该模型的概念化与公认的科学原则和预期的系统行为一致。所有这些结果都支持了模型的有效性和可信度。此外,还提出了进一步改进模型的机会。

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