气候变化领域集成服务门户(CCPortal): Physics-inspired integrated space-time artificial neural networks for regional groundwater flow modeling

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DOI	10.5194/hess-24-5759-2020
	Physics-inspired integrated space-time artificial neural networks for regional groundwater flow modeling
	Ghaseminejad A.; Uddameri V.
发表日期	2020
ISSN	1027-5606
起始页码	5759
结束页码	5779
卷号	24 期号:12
英文摘要	An integrated space-time artificial neural network (ANN) model inspired by the governing groundwater flow equation was developed to test whether a single ANN is capable of modeling regional groundwater flow systems. Modelindependent entropy measures and random forest (RF)-based feature selection procedures were used to identify suitable inputs for ANNs. L2 regularization, five-fold cross-validation, and an adaptive stochastic gradient descent (ADAM) algorithm led to a parsimonious ANN model for a 30 691 km2 agriculturally intensive area in the Ogallala Aquifer of Texas. The model testing at 38 independent wells during the 1956-2008 calibration period showed no overfitting issues and highlighted the model's ability to capture both the observed spatial dependence and temporal variability. The forecasting period (2009-2015) was marked by extreme climate variability in the region and served to evaluate the extrapolation capabilities of the model. While ANN models are universal interpolators, the model was able to capture the general trends and provide groundwater level estimates that were better than using historical means. Model sensitivity analysis indicated that pumping was the most sensitive process. Incorporation of spatial variability was more critical than capturing temporal persistence. The use of the standardized precipitation-evapotranspiration index (SPEI) as a surrogate for pumping was generally adequate but was unable to capture the heterogeneous groundwater extraction preferences of farmers under extreme climate conditions. © 2020 Author(s).
语种	英语
scopus关键词	Ability testing; Aquifers; Climate models; Decision trees; Gradient methods; Groundwater flow; Groundwater resources; Sensitivity analysis; Stochastic models; Stochastic systems; Well testing; Artificial neural network models; Groundwater extraction; Groundwater flow equation; Model sensitivity analysis; Regional groundwater flow; Regional groundwater flow modeling; Stochastic gradient descent; Temporal variability; Neural networks; algorithm; artificial neural network; entropy; evapotranspiration; flow modeling; groundwater flow; precipitation assessment; pumping; sensitivity analysis; temporal analysis; Ogallala Aquifer; Texas; United States
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159241
作者单位	Ghaseminejad, A., Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, United States; Uddameri, V., Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, United States
推荐引用方式 GB/T 7714	Ghaseminejad A.,Uddameri V.. Physics-inspired integrated space-time artificial neural networks for regional groundwater flow modeling[J],2020,24(12).
APA	Ghaseminejad A.,&Uddameri V..(2020).Physics-inspired integrated space-time artificial neural networks for regional groundwater flow modeling.Hydrology and Earth System Sciences,24(12).
MLA	Ghaseminejad A.,et al."Physics-inspired integrated space-time artificial neural networks for regional groundwater flow modeling".Hydrology and Earth System Sciences 24.12(2020).