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首页> 外文期刊>Journal of Hydroinformatics >A global and efficient multi-objective auto-calibration and uncertainty estimation method for water quality catchment models
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A global and efficient multi-objective auto-calibration and uncertainty estimation method for water quality catchment models

机译：一种全局高效的水质流域模型多目标自动校正与不确定性估计方法

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摘要

Catchment water quality models have many parameters, several output variables and a complex structure leading to multiple minima in the objective function. General uncertainty/optimization methods based on random sampling (e.g. GLUE) or local methods (e.g. PEST) are often not applicable for theoretical or practical reasons. This paper presents "ParaSol", a method that performs optimization and uncertainty analysis for complex models such as distributed water quality models. Optimization is done by adapting the Shuffled Complex Evolution algorithm (SCE-UA) to account for multi-objective problems and for large numbers of parameters. The simulations performed by the SCE-UA are used further for uncertainty analysis and thereby focus the uncertainty analysis on solutions near the optimum/optima. Two methods have been developed that select "good" results out of these simulations based on an objective threshold. The first method is based on x~2 statistics to delineate the confidence regions around the optimum/optima and the second method uses Bayesian statistics to define high probability regions. The ParaSol method was applied to a simple bucket model and to a Soil and Water Assessment Tool (SWAT) model of Honey Creek, OH, USA. The bucket model case showed the success of the method in finding the minimum and the applicability of the statistics under importance sampling. Both cases showed that the confidence regions are very small when the x~2 statistics are used and even smaller when using the Bayesian statistics. By comparing the ParaSol uncertainty results to those derived from 500,000 Monte Carlo simulations it was shown that the SCE-UA sampling used for ParaSol was more effective and efficient, as none of the Monte Carlo samples were close to the minimum or even within the confidence region defined by ParaSol.
机译：流域水质模型具有许多参数，多个输出变量和复杂的结构，导致目标函数中的多个最小值。基于理论或实践原因，基于随机采样（例如GLUE）或局部方法（例如PEST）的一般不确定性/优化方法通常不适用。本文介绍了“ ParaSol”，一种对复杂模型（例如分布式水质模型）执行优化和不确定性分析的方法。通过调整混洗复杂演化算法（SCE-UA）可以解决多目标问题和大量参数，从而实现优化。 SCE-UA进行的仿真进一步用于不确定性分析，从而将不确定性分析的重点放在接近最佳/最优值的解决方案上。已经开发出两种方法，可以基于客观阈值从这些模拟中选择“良好”结果。第一种方法基于x〜2统计量来描述最佳/最优值周围的置信区域，第二种方法使用贝叶斯统计量来定义高概率区域。将ParaSol方法应用于简单的桶模型以及美国俄亥俄州Honey Creek的土壤和水评估工具（SWAT）模型。桶模型案例显示了该方法在找到重要性样本下的最小值和统计量的适用性方面的成功。两种情况都表明，当使用x〜2统计量时，置信区域很小，而使用贝叶斯统计量时，甚至更小。通过将ParaSol不确定性结果与500,000蒙特卡洛模拟得出的结果进行比较，结果表明，用于ParaSol的SCE-UA采样更加有效，因为没有一个蒙特卡洛采样值接近最小值甚至在置信区间内由ParaSol定义。

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