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DOI | 10.1029/2018WR024301 |
A Spatially Enhanced Data-Driven Multimodel to Improve Groundwater Forecasts in the High Plains Aquifer, USA | |
Amaranto, A.1,2; Munoz-Arriola, F.1,3; Solomatine, D. P.2,4,5; Corzo, G.2 | |
2019-07-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:7页码:5941-5961 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Netherlands; Russia |
英文摘要 | The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two-step data-driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells across the High Plains aquifer (USA). The modeling framework includes a method to choose the most relevant input variables and time lags; an assessment of the effect of exogenous variables on the predictive capabilities of models; and the estimation of the forecast skill based on the Nash-Sutcliffe efficiency (NSE) index, the normalized root mean square error, and the coefficient of determination (R-2). Then, for the ANNs with low- accuracy, a MultiModel Combination (MuMoC) based on a hybrid of ANN and an instance-based learning method is applied. MuMoC uses forecasts from neighboring wells to improve the accuracy of ANNs. An exhaustive-search optimization algorithm is employed to select the best neighboring wells based on the cross correlation and predictive accuracy criteria. The results show high average ANN forecasting skills across the aquifer (average NSE > 0.9). Spatially distributed metrics of performance showed also higher error in areas of strong interaction between hydrometeorological forcings, irrigation intensity, and the aquifer. In those areas, the integration of the spatial information into MuMoC leads to an improvement of the model accuracy (NSE increased by 0.12), with peaks higher than 0.3 when the optimization objectives for selecting the neighbors were maximized.tT |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000481444700043 |
WOS关键词 | ARTIFICIAL NEURAL-NETWORK ; INPUT VARIABLE SELECTION ; PREDICTIVE CAPABILITIES ; MODELING TECHNIQUES ; PART 2 ; HYDROLOGY ; SUSTAINABILITY ; SENSITIVITY ; ALGORITHMS ; MANAGEMENT |
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/184864 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Nebraska, Dept Biol Syst Engn, Lincoln, NE 68588 USA; 2.Inst Water Educ, IHE Delft, Hydroinformat Chair Grp, Delft, Netherlands; 3.Univ Nebraska, Sch Nat Resources, Lincoln, NE 68588 USA; 4.Delft Univ Technol, Water Resources Sect, Delft, Netherlands; 5.RAS, Water Problems Inst, Flood Hydrol Lab, Moscow, Russia |
推荐引用方式 GB/T 7714 | Amaranto, A.,Munoz-Arriola, F.,Solomatine, D. P.,et al. A Spatially Enhanced Data-Driven Multimodel to Improve Groundwater Forecasts in the High Plains Aquifer, USA[J]. WATER RESOURCES RESEARCH,2019,55(7):5941-5961. |
APA | Amaranto, A.,Munoz-Arriola, F.,Solomatine, D. P.,&Corzo, G..(2019).A Spatially Enhanced Data-Driven Multimodel to Improve Groundwater Forecasts in the High Plains Aquifer, USA.WATER RESOURCES RESEARCH,55(7),5941-5961. |
MLA | Amaranto, A.,et al."A Spatially Enhanced Data-Driven Multimodel to Improve Groundwater Forecasts in the High Plains Aquifer, USA".WATER RESOURCES RESEARCH 55.7(2019):5941-5961. |
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