干旱区生态环境知识资源中心(Arid): Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques

Arid

DOI	10.1016/j.marpolbul.2024.116645
	Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques
	El-Rawy, Mustafa; Wahba, Mohamed; Fathi, Heba; Alshehri, Fahad; Abdalla, Fathy; El Attar, Raafat M.
通讯作者	El-Rawy, M
来源期刊	MARINE POLLUTION BULLETIN
ISSN	0025-326X
EISSN	1879-3363
出版年	2024
卷号	205
英文摘要	Assessing water quality in arid regions is vital due to scarce resources, impacting health and sustainable management.This study examines groundwater quality in Assuit Governorate, Egypt, using Principal Component Analysis, GIS, and Machine Learning Techniques. Data from 217 wells across 12 parameters were analyzed, including TDS, EC, Cl-, Fe++, Ca++, Mg++, Na+, SO4-, Mn++, HCO3-, K+, and pH. The Water Quality Index (WQI) was calculated, and ArcGIS mapped its spatial distribution. Machine learning algorithms, including Ridge Regression, XGBoost, Decision Tree, Random Forest, and K-Nearest Neighbors, were used for predictive analysis. Higher concentrations of Na, K, Ca, Mg, Mn, and Fe were correlated with industrial and densely populated areas. Most samples exhibited excellent or good quality, with a small percentage unsuitable for consumption. Ridge Regression showed the lowest MAPE rates (0.22 % training, 0.26 % in testing). This research highlights the importance of advanced machine learning for sustainable groundwater management in arid regions. Thus, our results could provide valuable assistance to both national and local authorities involved in water management decisions, particularly for water resource managers and decision-makers. This information can aid in the development of regulations aimed at safeguarding and sustainably managing groundwater resources, which are essential for the overall prosperity of the country.
英文关键词	Water quality index Groundwater quality Deep neural network Sensitivity analysis Subset regression model Egypt
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001260550200001
WOS关键词	WATER-QUALITY ; RIDGE-REGRESSION ; GOVERNORATE ; PREDICTION ; INDEX ; HYDROGEOCHEMISTRY ; AQUIFER ; SYSTEM
WOS类目	Environmental Sciences ; Marine & Freshwater Biology
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404887
推荐引用方式 GB/T 7714	El-Rawy, Mustafa,Wahba, Mohamed,Fathi, Heba,et al. Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques[J],2024,205.
APA	El-Rawy, Mustafa,Wahba, Mohamed,Fathi, Heba,Alshehri, Fahad,Abdalla, Fathy,&El Attar, Raafat M..(2024).Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques.MARINE POLLUTION BULLETIN,205.
MLA	El-Rawy, Mustafa,et al."Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques".MARINE POLLUTION BULLETIN 205(2024).