干旱区生态环境知识资源中心(Arid): Evaluation of Groundwater Quality for Irrigation in Deep Aquifers Using Multiple Graphical and Indexing Approaches Supported with Machine Learning Models and GIS Techniques, Souf Valley, Algeria

Arid

DOI	10.3390/w15010182
	Evaluation of Groundwater Quality for Irrigation in Deep Aquifers Using Multiple Graphical and Indexing Approaches Supported with Machine Learning Models and GIS Techniques, Souf Valley, Algeria
	Eid, Mohamed Hamdy; Elbagory, Mohssen; Tamma, Ahmed A.; Gad, Mohamed; Elsayed, Salah; Hussein, Hend; Moghanm, Farahat S.; Omara, Alaa El-Dein; Kovacs, Attila; Peter, Szucs
通讯作者	Eid, MH
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:1
英文摘要	Irrigation has made a significant contribution to supporting the population's expanding food demands, as well as promoting economic growth in irrigated regions. The current investigation was carried out in order to estimate the quality of the groundwater for agricultural viability in the Algerian Desert using various water quality indices and geographic information systems (GIS). In addition, support vector machine regression (SVMR) was applied to forecast eight irrigation water quality indices (IWQIs), such as the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), potential salinity (PS), Kelly index (KI), permeability index (PI), potential salinity (PS), permeability index (PI), and residual sodium carbonate (RSC). Several physicochemical variables, such as temperature (T degrees), hydrogen ion concentration (pH), total dissolved solids (TDS), electrical conductivity (EC), K+, Na2+, Mg2+, Ca2+, Cl-, SO42-, HCO3-, CO32-, and NO3-, were measured from 45 deep groundwater wells. The hydrochemical facies of the groundwater resources were Ca-Mg-Cl/SO4 and Na-Cl-, which revealed evaporation, reverse ion exchange, and rock-water interaction processes. The IWQI, Na%, SAR, SSP, KI, PS, PI, and RSC showed mean values of 50.78, 43.07, 4.85, 41.78, 0.74, 29.60, 45.65, and -20.44, respectively. For instance, the IWQI for the obtained results indicated that the groundwater samples were categorized into high restriction to moderate restriction for irrigation purposes, which can only be used for plants that are highly salt tolerant. The SVMR model produced robust estimates for eight IWQIs in calibration (Cal.), with R-2 values varying between 0.90 and 0.97. Furthermore, in validation (Val.), R-2 values between 0.88 and 0.95 were achieved using the SVMR model, which produced reliable estimates for eight IWQIs. These findings support the feasibility of using IWQIs and SVMR models for the evaluation and management of the groundwater of complex terminal aquifers for irrigation. Finally, the combination of IWQIs, SVMR, and GIS was effective and an applicable technique for interpreting and forecasting the irrigation water quality used in both arid and semi-arid regions.
英文关键词	hydrogeochemistry reverse ion exchange water quality indices irrigation
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000909637800001
WOS关键词	RIVER WATER-QUALITY ; HYDROGEOCHEMICAL PROCESSES ; SUITABILITY ; PURPOSES ; BASIN ; CLASSIFICATION ; IDENTIFICATION ; EVOLUTION ; DRINKING ; MECHANISMS
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398975
推荐引用方式 GB/T 7714	Eid, Mohamed Hamdy,Elbagory, Mohssen,Tamma, Ahmed A.,et al. Evaluation of Groundwater Quality for Irrigation in Deep Aquifers Using Multiple Graphical and Indexing Approaches Supported with Machine Learning Models and GIS Techniques, Souf Valley, Algeria[J],2023,15(1).
APA	Eid, Mohamed Hamdy.,Elbagory, Mohssen.,Tamma, Ahmed A..,Gad, Mohamed.,Elsayed, Salah.,...&Peter, Szucs.(2023).Evaluation of Groundwater Quality for Irrigation in Deep Aquifers Using Multiple Graphical and Indexing Approaches Supported with Machine Learning Models and GIS Techniques, Souf Valley, Algeria.WATER,15(1).
MLA	Eid, Mohamed Hamdy,et al."Evaluation of Groundwater Quality for Irrigation in Deep Aquifers Using Multiple Graphical and Indexing Approaches Supported with Machine Learning Models and GIS Techniques, Souf Valley, Algeria".WATER 15.1(2023).