Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026061 |
Reduced-Dimensional Gaussian Process Machine Learning for Groundwater Allocation Planning Using Swarm Theory | |
Siade, Adam J.1,2,3; Cui, Tao3; Karelse, Robert N.4; Hampton, Clive5 | |
2020-03-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2020 |
卷号 | 56期号:3 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia |
英文摘要 | Groundwater management and allocation planning involves a rigorous assessment of the performance of operational decisions such as extraction/injection rates on community and environmental objectives. Maximizing performance through numerical optimization can be essential for high-value resources and is often computationally infeasible due to long simulation model run times combined with nonconvex objectives and constraints. In order to mitigate these drawbacks, surrogate models can be used in place of complex models during the optimization process. There exist a number of machine learning techniques that can be used to develop a data-driven surrogate model. However, the curse of dimensionality, common to groundwater management, limits the use of these techniques due to the necessity for large training data sets. Even though it is now possible to handle large data sets, the generation of these data sets themselves remains computationally prohibitive as they require numerous simulations to produce accurate surrogates. In this study, we integrate a dimensionality reduction method using truncated singular value decomposition to reduce the number of decision variables, thereby reducing the size of the training data set needed. Correspondingly, we demonstrate a simple technique for acquiring an approximate minimax Latin Hypercube design from within the subspace. We also implement a novel technique for adaptive resampling through particle swarm optimization in order to maintain accuracy of the surrogate model throughout the optimization process. The resulting accurate surrogate model for the Perth regional aquifer system of Western Australia runs in a matter of seconds. Adopting this approach can produce timely solutions, making formal optimization tractable for practitioners. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000538000800027 |
WOS关键词 | GLOBAL OPTIMIZATION ; NEURAL-NETWORKS ; MANAGEMENT ; EFFICIENT ; DESIGN ; MODELS ; SUPPORT ; STRATEGIES ; PREDICTION ; REGRESSION |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/280569 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Western Australia, Sch Earth Sci, Crawley, WA, Australia; 2.Natl Ctr Groundwater Res & Training, Bedford Pk, SA, Australia; 3.CSIRO Land & Water, Wembley, WA, Australia; 4.Western Australia Dept Water & Environm Regulat, Joondalup, WA, Australia; 5.Water Corp Western Australia, Leederville, WA, Australia |
推荐引用方式 GB/T 7714 | Siade, Adam J.,Cui, Tao,Karelse, Robert N.,et al. Reduced-Dimensional Gaussian Process Machine Learning for Groundwater Allocation Planning Using Swarm Theory[J]. WATER RESOURCES RESEARCH,2020,56(3). |
APA | Siade, Adam J.,Cui, Tao,Karelse, Robert N.,&Hampton, Clive.(2020).Reduced-Dimensional Gaussian Process Machine Learning for Groundwater Allocation Planning Using Swarm Theory.WATER RESOURCES RESEARCH,56(3). |
MLA | Siade, Adam J.,et al."Reduced-Dimensional Gaussian Process Machine Learning for Groundwater Allocation Planning Using Swarm Theory".WATER RESOURCES RESEARCH 56.3(2020). |
条目包含的文件 | 条目无相关文件。 |
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