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首页> 外文期刊>Ground water >Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models
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Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models

机译：使用机器学习方法减少地下水模型的预测误差

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摘要

Quantitative analyses of groundwater flow and transport typically rely on a physically-based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data-driven models (DDMs) to reduce the predictive error of physically-based groundwater models. Two machine learning techniques, the instance-based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real-world case studies of the Republican River Compact Administration model and the Spokane Valley-Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root-mean-square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically-based model.
机译：地下水流和运输的定量分析通常依赖于基于物理的模型，该模型固有地容易出错。即使在校准模型的输出中，模型结构，参数和数据中的错误也会导致随机和系统错误。我们开发了互补的数据驱动模型（DDM），以减少基于物理的地下水模型的预测误差。两种机器学习技术（基于实例的加权和支持向量回归）用于构建DDM。使用共和党河契约管理模型和斯波坎谷地-拉德鲁姆草原模型的两个真实案例研究说明了这种方法。这两种地下水模型具有不同的水文地质设置，参数化和校准方法。在第一个案例研究中，为数据预处理引入了聚类分析，以使DDM更加健壮和高效计算。 DDM将地下水模型的测压头的时间，空间和时空预测的均方根误差（RMSE）分别降低了82％，60％和48％。在第二个案例研究中，DDM将地下水模型测压头的时间预测的RMSE降低了77％。进一步证明，DDM的有效性取决于基于物理模型的错误中结构的存在和程度。

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  • 来源
    《Ground water》 |2014年第3期|448-460|共13页
  • 作者

    Tianfang Xu; Albert J. Valocchi; Jaesik Choi; Eyal Amir;

  • 作者单位

    Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL;

    Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801;

    Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801 ,Currently at Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720;

    Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801;

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