基于物理和机器学习辅助的海岸含水层系统高保真度管理方法的性能比较

IF 2.6 Q2 WATER RESOURCES
G. Kopsiaftis, Maria Kaselimi, Eftychios E. Protopapadakis, A. Voulodimos, A. Doulamis, N. Doulamis, A. Mantoglou
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引用次数: 2

摘要

在这项工作中,我们研究了各种低保真度海水入侵模型在沿海含水层管理问题中的性能。将变密度模型视为高保真度模型,并将抽水优化框架应用于假设的海岸含水层系统,以计算最佳抽水率,该最佳抽水率用作高保真度方法的基准。所检查的低保真度模型可分为两类:(1)基于物理的模型,包括几种广泛使用的锐界面近似变体;(2)机器学习辅助模型,旨在提高SI方法的效率。随机森林方法用于为原始锐界面模型创建空间自适应校正因子,这提高了其精度,而不影响其作为低保真度模型的效率。然后,在单一保真度优化方法中测试原始清晰界面和机器学习辅助模型。使用基于机器学习的SI模型计算的最佳泵送额定值充分近似于可变密度模型的解。机器学习辅助近似似乎是高保真度可变密度模型的一种很有前途的替代方法,可用于高保真度地下水管理框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems
In this work we investigate the performance of various lower-fidelity models of seawater intrusion in coastal aquifer management problems. The variable density model is considered as the high-fidelity model and a pumping optimization framework is applied on a hypothetical coastal aquifer system in order to calculate the optimal pumping rates which are used as a benchmark for the lower-fidelity approaches. The examined lower-fidelity models could be classified in two categories: (1) physics-based models, which include several widely used variations of the sharp-interface approximation and (2) machine learning assisted models, which aim to improve the efficiency of the SI approach. The Random Forest method was utilized to create a spatially adaptive correction factor for the original sharp-interface model, which improves its accuracy without compromising its efficiency as a lower-fidelity model. Both the original sharp-interface and Machine Learning assisted model are then tested in a single-fidelity optimization method. The optimal pumping rated which were calculated using the Machine Learning based SI model sufficiently approximate the solution from the variable density model. The Machine Learning assisted approximation seems to be a promising surrogate for the high-fidelity, variable density model and could be utilized in multi-fidelity groundwater management frameworks.
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来源期刊
Frontiers in Water
Frontiers in Water WATER RESOURCES-
CiteScore
4.00
自引率
6.90%
发文量
224
审稿时长
13 weeks
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