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Identifiability and uncertainty analysis of the River Water Quality Model No. 1 (RWQM1)

机译:河流水质1号模型(RWQM1)的可识别性和不确定性分析

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State of the art models as used in activated sludge modelling and recently proposed for river water quality modelling integrate the knowledge in a certain field. If applied to data from a specific site, such models are nearly always overparameterised. This raises the question of how many parameters can be fitted in a given context and how to find identifiable parameter subsets given the experimental layout, This problem is addressed for the kinetic parameters of a simplified version of the recently published river water quality model no. 1 (RWQM 1). The selection of practically identifiable parameter subsets is discussed for typical boundary conditions as a function of the measurement layout. Two methods for identifiable subset selection were applied and lead to nearly the same results. Assuming upstream and downstream measurements of dissolved substances to be available, only a few (5-8) model parameters appear to be identifiable. Extensive measurement campaigns with dedicated experiments seem to be required for successful calibration of RWQM 1. The estimated prior uncertainties of the model parameters are used to estimate the uncertainty of model predictions. Finally an estimate is provided for the maximum possible decrease in prediction uncertainty achievable by a perfect determination of the values of the identifiable model parameters. [References: 21]
机译:用于活性污泥建模的最新模型以及最近提出的用于河水水质建模的模型综合了特定领域的知识。如果将这些模型应用于来自特定站点的数据,则几乎总是参数过大。这就提出了一个问题,即在给定的环境中可以拟合多少个参数,以及在给定的实验布局下如何找到可识别的参数子集。这个问题针对的是最近发布的河水质量模型No.1的简化版本的动力学参数。 1(RWQM 1)。根据测量布局,讨论了典型边界条件下实际可识别参数子集的选择。应用了两种可识别子集选择的方法,结果几乎相同。假设可以进行溶解物质的上游和下游测量,那么似乎只有几个(5-8)模型参数是可识别的。要成功校准RWQM 1,似乎需要进行专门的实验进行广泛的测量。估计的模型参数的先验不确定性用于估计模型预测的不确定性。最后,通过完美确定可识别模型参数的值,为最大可能的预测不确定性降低提供估计。 [参考:21]

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