干旱区生态环境知识资源中心(Arid): Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods

Arid

DOI	10.1016/j.jhydrol.2024.131268
	Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods
	Nourani, Vahid; Paknezhad, Nardin Jabbarian; Mohammadisepasi, Sepideh; Zhang, Yongqiang
通讯作者	Nourani, V
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2024
卷号	636
英文摘要	Groundwater (GW) plays a crucial role in coastal aquifers and arid regions, serving as a lifeline for communities by providing a reliable and resilient water source, making its monitoring essential for sustainable water management. This study aimed at modeling GW via regionalization of the Gravity Recovery and Climate Experiment (GRACE) data based on two methods. The first method directly regionalized the GRACE data for modeling GW via in situ measurements, including the lake level, precipitation, temperature, observed GW and PenmanMonteith-Leuning (PML) evapotranspiration data. The second method included two stages, in the first stage, the GRACE data were downscaled via the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) data which contains satellite based precipitation, temperature, soil moisture, and snow water equivalent data. In the second stage, the downscaled GRACE was bias corrected to provide regionalized data. Artificial intelligence models consist of shallow networks (Feed Forward Neural Network (FFNN), Adaptive neuro fuzzy (ANFIS), Support Vector Machine (SVR)), the ensemble of shallow networks and Long-Short Term Memory (LSTM) deep learning method were employed in the modeling process and the observed GW level data were targeted for the regionalization. The Link CluE clustering ensemble method was implemented to cluster the piezometers of the aquifer to separate different GW patterns in the area. The proposed methodology was examined over the Miandoab plain, one of the sub-basins of the Lake Urmia, located in Northwest Iran. The modeling results demonstrated that the first method could exhibit superior performance with the Nash-Sutcliffe Efficiency (NSE) of up to 17% higher than the second method. Thus, using in situ observed data for downscaling proved to be more accurate than relying on the data based on the satellite imagery. The results indicated that the ensemble of shallow networks could lead to more precise results than using the deep and shallow learning models, individually, where the NSE for the ensemble of shallow networks was up to 50% higher compared to the LSTM model.
英文关键词	Groundwater GRACE data Downscaling Clustering Lake Urmia
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001238793100001
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404583
推荐引用方式 GB/T 7714	Nourani, Vahid,Paknezhad, Nardin Jabbarian,Mohammadisepasi, Sepideh,et al. Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods[J],2024,636.
APA	Nourani, Vahid,Paknezhad, Nardin Jabbarian,Mohammadisepasi, Sepideh,&Zhang, Yongqiang.(2024).Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods.JOURNAL OF HYDROLOGY,636.
MLA	Nourani, Vahid,et al."Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods".JOURNAL OF HYDROLOGY 636(2024).