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首页> 外文期刊>Transactions of the ASABE >Detection of overparameterization and overfitting in an automatic calibration of SWAT. (Special Issue: Soil and water assessment tool (SWAT) modeling technology: current status.)
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Detection of overparameterization and overfitting in an automatic calibration of SWAT. (Special Issue: Soil and water assessment tool (SWAT) modeling technology: current status.)

机译：在SWAT自动校准中检测过度参数化和过度拟合。 （特刊：水土评估工具（SWAT）建模技术：当前状态。）

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Distributed hydrologic models based on small-scale physical processes tend to have a large number of parameters to represent spatial heterogeneity. This characteristic requires the use of a large number of parameters in model calibration. It is a common view that calibration with a large number parameters produces overparameterization and overfitting. Recent work using prior information, spatial information, and constraints on parameters for regularization of the calibration problem has improved model predictions using a few dozen parameters. We demonstrate that the Soil and Water Assessment Tool (SWAT) and the information associated with a SWAT watershed setup provide a regularized problem with many of recently published regularization techniques already utilized in SWAT. Our hypothesis is that the Soil and Water Assessment Tool (SWAT) regularizes the inverse problem so that a stable solution can be obtained for calibration of SWAT using a very large number of parameters, where very large means up to 10,000 calibration parameters. In this study, a two-objective calibration genetic algorithm based on a non-dominated sorting genetic algorithm (NSGA-II) was used to calibrate the Blue River basin in Oklahoma. We introduce the use of intermediate solutions found by the genetic algorithm to test identification of calibration parameters and diagnose model overfitting. Defining identification as the capability of a model to constrain the estimation of parameters, we introduced a method for statistically testing for changes from the initial uniform distribution of each parameter. We found that all 4,198 parameters used to calculate the Blue River SWAT model were identified. Diagnostic comparisons of goodness-of-fit measures for the calibration and validation periods provided strong evidence that the model was not overfitted.
机译：基于小规模物理过程的分布式水文模型往往具有大量代表空间异质性的参数。此特性要求在模型校准中使用大量参数。常见的观点是，使用大量参数进行校准会导致过度参数化和过度拟合。使用先验信息，空间信息和对参数进行约束以进行校准问题正则化的最新工作已使用几十个参数改进了模型预测。我们证明了土壤和水评估工具（SWAT）以及与SWAT分水岭设置相关的信息为SWAT中已经使用的许多最新发布的正则化技术提供了正则化问题。我们的假设是土壤和水评估工具（SWAT）规范了反问题，因此可以使用大量参数来获得稳定的SWAT校准解决方案，其中很大意味着最多10,000个校准参数。在这项研究中，基于非支配排序遗传算法（NSGA-II）的两目标校准遗传算法被用于校准俄克拉荷马州的蓝河流域。我们介绍了遗传算法发现的中间解决方案的使用，以测试对校准参数的识别并诊断模型过度拟合。将识别定义为模型可约束参数估计的功能，我们引入了一种统计方法来测试每个参数的初始均匀分布的变化。我们发现，已确定用于计算Blue River SWAT模型的所有4,198个参数。在校准和验证期间拟合优度度量的诊断比较提供了有力的证据，表明该模型未过度拟合。

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