Kanso A, Gromaire M C, Gaume E, Tassin B, Chebbo G
CEREVE (Centre d'Enseignement et de Recherche sur l'Eau, la Ville et l'Environnement), Ecole Nationale des Ponts et Chaussées, 6-8 avenue Blaise-Pascal, Cité Descartes, 77455 Marne-la-Vallée 2, France.
Water Sci Technol. 2003;47(4):77-84.
In environmental modelling, estimating the confidence level in conceptual model parameters is necessary but difficult. Having a realistic estimation of the uncertainties related to the parameters is necessary i) to assess the possible origin of the calibration difficulties (correlation between model parameters for instance), and ii) to evaluate the prediction confidence limits of the calibrated model. In this paper, an application of the Metropolis algorithm, a general Monte Carlo Markov chain sampling method, for the calibration of a four-parameter lumped urban stormwater quality model is presented. Unlike traditional optimisation approaches, the Metropolis algorithm identifies not only a "best parameter set", but a probability distribution of parameters according to measured data. The studied model includes classical formulations for the pollutant accumulation during dry weather period and their washoff during a rainfall event. Results indicate mathematical shortcomings in the pollutant accumulation formulation used.
在环境建模中，估计概念模型参数的置信水平是必要的，但也是困难的。对与参数相关的不确定性进行现实估计是必要的，一是为了评估校准困难的可能来源（例如模型参数之间的相关性），二是为了评估校准模型的预测置信限。本文介绍了一种通用的蒙特卡罗马尔可夫链采样方法—— metropolis算法在一个四参数集总城市雨水水质模型校准中的应用。与传统的优化方法不同， metropolis算法不仅能识别出“最佳参数集”，还能根据实测数据确定参数的概率分布。所研究的模型包括旱季污染物累积及其在降雨事件中的冲刷的经典公式。结果表明所使用的污染物累积公式存在数学缺陷。
