干旱区生态环境知识资源中心(Arid): Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones

Arid

DOI	10.1016/j.ejrs.2024.02.006
	Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones
	Al-Ruzouq, Rami; Shanableh, Abdallah; Jena, Ratiranjan; Mukherjee, Sunanda; Khalil, Mohamad Ali; Gibril, Mohamed Barakat A.; Pradhan, Biswajeet; Hammouri, Nezar Atalla
通讯作者	Jena, R
来源期刊	EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES
ISSN	1110-9823
EISSN	2090-2476
出版年	2024
卷号	27 期号:2 页码:178-191
英文摘要	The increase in water demand and the scarcity of fresh water in arid regions have contributed to the depletion of groundwater. Artificial Groundwater Recharge (AGR) is an advanced strategy that contributes to combating water shortage issues. Limited efforts have been exerted to evaluate and demarcate AGR potential zones in the United Arab Emirates (UAE). The current study aims to delineate AGR potential zone mapping using the traditional analytical hierarchy process (AHP) and a hybrid deep learning model namely, Convolutional Neural Network-Xtreme Gradient Boosting (CNN-XGB) was used for the optimal prediction-based suitability assessment. A total of nine hydrogeological factors were considered for AGR mapping. First, the influence of each parameter was determined based on expert opinion and literature reviews for the AHP approach (0.007 consistency ratio). Second, a hybrid CNN-XGB model (90.8 % accuracy) predicted the AGR and non-AGR classes as part of binary classification and generated an AGR potential zone map. Moreover, the contributing factors were analyzed deeply for the AGR site selection to understand the intercorrelation, importance, and prediction interaction. Using both approaches, a comparative assessment was conducted in the eastern, central, and western parts of Sharjah. The AGR zone based on the CNN-XGB model achieved a precision of (0.8168), recall (0.7873), and F1score (0.8018). The critical contributing factors for AGR mapping were found to be geology (20%), geomorphology (15%), rainfall (10%), and groundwater level (10%). The AGR map is expected to help explore new sites with potentially higher favourability to retain water, deal with water scarcity, and improve water management in the UAE.
英文关键词	Artificial groundwater recharge Remote sensing (RS) GIS Multicriteria analysis CNN-XGB model United Arab Emirates
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001205827400001
WOS关键词	MULTICRITERIA DECISION-MAKING ; GIS ; IDENTIFICATION ; BASIN ; AREAS
WOS类目	Environmental Sciences ; Remote Sensing
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403479
推荐引用方式 GB/T 7714	Al-Ruzouq, Rami,Shanableh, Abdallah,Jena, Ratiranjan,et al. Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones[J],2024,27(2):178-191.
APA	Al-Ruzouq, Rami.,Shanableh, Abdallah.,Jena, Ratiranjan.,Mukherjee, Sunanda.,Khalil, Mohamad Ali.,...&Hammouri, Nezar Atalla.(2024).Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones.EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES,27(2),178-191.
MLA	Al-Ruzouq, Rami,et al."Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones".EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES 27.2(2024):178-191.