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An improved parameter estimation procedure in lake modelling

机译:湖泊建模中一种改进的参数估计程序

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This paper proposes an alternative parameter estimation procedure. Usually the parameters that yield the highest sensitivity are calibrated by trial and error or by an automatic calibration procedure. If the number of parameters to be calibrated is more than 3-5, this procedure is very time consuming. The presented procedure is based on the assumption that the biological components in an ecosystem model attempt to develop such properties that they become best possible survivors, i.e. develop as much biomass as possible. It has previously been proposed that the survival of the entire ecosystem can be measured by use of the thermodynamic function exergy, which measures the distance from thermodynamic equilibrium and accounts for the biomass and its information content. If these assumptions are correct, it should be possible to determine the combination of parameters which gives the highest exergy. The paper presents the use of this idea in combination with a normal calibration. The parameters, which are less known from the literature and still yield a relatively high sensitivity, are determined by use of the above-mentioned exergy principle. The parameters that are known within relatively narrow ranges from the literature are calibrated by the normal procedure. The method has been used on a concrete lake modelling study and given good results. This combination method seems therefore to offer clear advantages, particularly for models with a relatively high number of sensitive parameters (> 5), which otherwise would require a very cumbersome calibration.
机译:本文提出了一种可选的参数估计程序。通常,通过反复试验或通过自动校准程序来校准产生最高灵敏度的参数。如果要校准的参数数量大于3-5,则此过程非常耗时。提出的程序是基于这样的假设,即生态系统模型中的生物成分试图开发出使其成为最可能的幸存者的特性,即,尽可能多地开发生物质。先前已经提出,可以通过使用热力学函数本能来测量整个生态系统的生存,所述热力学函数本能用于测量距热力学平衡的距离并说明生物量及其信息含量。如果这些假设是正确的,则应该有可能确定给出最大火用能的参数组合。本文介绍了这种想法与常规校准的结合使用。这些参数是文献中鲜为人知的,但仍具有较高的灵敏度,这些参数是通过使用上述的本能原理确定的。在文献中相对狭窄的范围内已知的参数通过常规程序进行校准。该方法已用于具体的湖泊建模研究,并取得了良好的效果。因此,这种组合方法似乎具有明显的优势,特别是对于具有相对较高数量的敏感参数(> 5)的模型,否则将需要非常麻烦的校准。

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