干旱区生态环境知识资源中心(Arid): Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study

Arid

DOI	10.3390/w15040694
	Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study
	Ibrahim, Hekmat; Yaseen, Zaher Mundher; Scholz, Miklas; Ali, Mumtaz; Gad, Mohamed; Elsayed, Salah; Khadr, Mosaad; Hussein, Hend; Ibrahim, Hazem H.; Eid, Mohamed Hamdy; Kovacs, Attila; Peter, Szucs; Khalifa, Moataz M.
通讯作者	Yaseen, ZM
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:4
英文摘要	Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T degrees, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl-, SO42-, HCO3-, CO32-, and NO3-, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock-water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training determination coefficient (R-2) (R-2 = 0.99 and 0.97) and testing (R-2 = 0.97 and 0.76). The presented models' promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments.
英文关键词	groundwater quality water quality indices irrigation machine learning hydrogeochemistry
类型	Article
语种	英语
开放获取类型	Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000942476600001
WOS关键词	LIMITED HYDROGEOLOGICAL DATA ; NUBIAN SANDSTONE ; WESTERN DESERT ; KHARGA OASIS ; GREY MODEL ; PURPOSES ; RIVER ; SUITABILITY ; IDENTIFICATION ; BASIN
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/2XILL650/398990
推荐引用方式 GB/T 7714	Ibrahim, Hekmat,Yaseen, Zaher Mundher,Scholz, Miklas,et al. Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study[J],2023,15(4).
APA	Ibrahim, Hekmat.,Yaseen, Zaher Mundher.,Scholz, Miklas.,Ali, Mumtaz.,Gad, Mohamed.,...&Khalifa, Moataz M..(2023).Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study.WATER,15(4).
MLA	Ibrahim, Hekmat,et al."Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study".WATER 15.4(2023).