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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models
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OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models

机译：OptIC项目：陆地生物地球化学模型参数估计优化技术的比较

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摘要

We describe results of a project known as OptIC (Optimisation InterComparison) for comparison of parameter estimation methods in terrestrial biogeochemical models. A highly simplified test model was used to generate pseudo-data to which noise with different characteristics was added. Participants in the OptIC project were asked to estimate the model parameters used to generate this data, and to predict model variables into the future. Ten participants contributed results using one of the following methods: Levenberg-Marquardt, adjoint, Kalman filter, Markov chain Monte Carlo and genetic algorithm. Methods differed in how they locate the minimum (gradient-descent or global search), how observations are processed (all at once sequentially), or the number of iterations used, or assumptions about the statistics (some methods assume Gaussian probability density functions; others do not). We found the different methods equally successful at estimating the parameters in our application. The biggest variation in parameter estimates arose from the choice of cost function, not the choice of optimization method. Relatively poor results were obtained when the model-data mismatch in the cost function included weights that were instantaneously dependent on noisy observations. This was the case even when the magnitude of residuals varied with the magnitude of observations. Missing data caused estimates to be more scattered, and the uncertainty of predictions increased correspondingly. All methods gave biased results when the noise was temporally correlated or non-Gaussian, or when incorrect model forcing was used. Our results highlight the need for care in choosing the error model in any optimization.
机译：我们描述了一个称为OptIC（优化比较）的项目结果，用于比较陆地生物地球化学模型中的参数估计方法。使用高度简化的测试模型来生成伪数据，在伪数据中添加了具有不同特征的噪声。要求OptIC项目的参与者估计用于生成此数据的模型参数，并预测未来的模型变量。十名参与者使用以下方法之一贡献了结果：Levenberg-Marquardt，伴随，卡尔曼滤波器，马尔可夫链蒙特卡洛和遗传算法。方法的不同之处在于它们如何定位最小值（梯度下降或全局搜索），如何处理观测结果（依次一次全部进行），使用的迭代次数或对统计的假设（某些方法假设为高斯概率密度函数;其他方法为不要）。我们发现不同的方法在估算应用程序中的参数方面同样成功。参数估计的最大变化来自于成本函数的选择，而不是优化方法的选择。当成本函数中的模型数据不匹配包括权重瞬时取决于噪声观测值时，则获得相对较差的结果。即使残差的大小随观察值的大小而变化，情况也是如此。缺少数据导致估计值更加分散，并且预测的不确定性相应增加。当噪声在时间上相关或不是高斯型，或者使用了不正确的模型强制时，所有方法给出的结果都有偏差。我们的结果表明在任何优化中选择错误模型时都需要谨慎。

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