Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016WR019933 |
Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US | |
Sahoo, S.1; Russo, T. A.1; Elliott, J.2,3; Foster, I.2,3,4 | |
2017-05-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:5 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Climate, groundwater extraction, and surface water flows have complex nonlinear relationships with groundwater level in agricultural regions. To better understand the relative importance of each driver and predict groundwater level change, we develop a new ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, as an alternative to complex and computationally expensive physical models. We apply and evaluate this new approach in the context of two aquifer systems supporting agricultural production in the United States: the High Plains aquifer (HPA) and the Mississippi River Valley alluvial aquifer (MRVA). We select input data sets by using a combination of mutual information, genetic algorithms, and lag analysis, and then use the selected data sets in a Multilayer Perceptron network architecture to simulate seasonal groundwater level change. As expected, model results suggest that irrigation demand has the highest influence on groundwater level change for a majority of the wells. The subset of groundwater observations not used in model training or cross-validation correlates strongly (R>0.8) with model results for 88 and 83% of the wells in the HPA and MRVA, respectively. In both aquifer systems, the error in the modeled cumulative groundwater level change during testing (2003-2012) was less than 2 m over a majority of the area. We conclude that our modeling framework can serve as an alternative approach to simulating groundwater level change and water availability, especially in regions where subsurface properties are unknown. |
英文关键词 | groundwater model machine learning irrigation demand High Plains aquifer Mississippi aquifer climate |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000403712100023 |
WOS关键词 | ARTIFICIAL NEURAL-NETWORKS ; HYDROLOGIC TIME-SERIES ; HIGH-PLAINS AQUIFER ; MUTUAL INFORMATION ; CLIMATIC VARIABILITY ; CHANGE IMPACTS ; WATER ; SYSTEM ; VARIABLES ; SELECTION |
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/21394 |
专题 | 资源环境科学 |
作者单位 | 1.Penn State Univ, Dept Geosci, University Pk, PA 16802 USA; 2.Univ Chicago, Computat Inst, Chicago, IL 60637 USA; 3.Argonne Natl Lab, Math & Comp Sci Div, Lemont, IL USA; 4.Univ Chicago, Dept Comp Sci, Chicago, IL 60637 USA |
推荐引用方式 GB/T 7714 | Sahoo, S.,Russo, T. A.,Elliott, J.,et al. Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US[J]. WATER RESOURCES RESEARCH,2017,53(5). |
APA | Sahoo, S.,Russo, T. A.,Elliott, J.,&Foster, I..(2017).Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US.WATER RESOURCES RESEARCH,53(5). |
MLA | Sahoo, S.,et al."Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US".WATER RESOURCES RESEARCH 53.5(2017). |
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