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首页> 外文期刊>Journal of Hydroinformatics >Optimal groundwater management using state-space surrogate models: a case study for an arid coastal region
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Optimal groundwater management using state-space surrogate models: a case study for an arid coastal region

机译：使用状态空间替代模型的最佳地下水管理：以干旱沿海地区为例

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A surrogate modeling framework is developed in this study to circumvent the computational burden of high-fidelity numerical groundwater models for arid coastal aquifers. Two different surrogate models, namely, artificial neural network (ANN) and Gaussian process model (GPM) are trained to replace the computationally expensive numerical flow and transport model OpenGeoSys. A novel time-dependent training scheme is introduced which helps the surrogates in tracking the discrete-time state-space trajectories of the high-fidelity model, thereby making them suitable for variable-time simulations. The surrogates are also tested in the extrapolation range corresponding to some extreme boundary conditions such as a very high rate of extraction. Both the surrogates show comparable accuracy in efficiently approximating the numerical model response; however, ANN is found to be much faster than GPM for the size of the data used. The trained surrogates are then used in developing a long-term planning and management framework for analyzing feasible management scenarios in the coastal aquifer of Oman.
机译：在本研究中，开发了一个替代模型框架来规避干旱沿海含水层的高保真数值地下水模型的计算负担。训练了两种不同的替代模型，即人工神经网络（ANN）和高斯过程模型（GPM），以取代计算量大的数值流和运输模型OpenGeoSys。引入了一种新颖的基于时间的训练方案，该方案可帮助代理人跟踪高保真模型的离散时间状态空间轨迹，从而使其适合于可变时间仿真。还在对应于某些极端边界条件（例如非常高的提取率）的外推范围内测试了替代物。两种替代方案在有效逼近数值模型响应方面均显示出相当的精度；但是，对于所用数据的大小，发现ANN比GPM快得多。经过培训的代理人随后被用于制定长期计划和管理框架，以分析阿曼沿海含水层中可行的管理方案。

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