干旱区生态环境知识资源中心(Arid): An Integrated GIS and Machine-Learning Technique for Groundwater Quality Assessment and Prediction in Southern Saudi Arabia

Arid

DOI	10.3390/w15132448
	An Integrated GIS and Machine-Learning Technique for Groundwater Quality Assessment and Prediction in Southern Saudi Arabia
	El-Rawy, Mustafa; Batelaan, Okke; Alshehri, Fahad; Almadani, Sattam; Ahmed, Mohamed S.; Elbeltagi, Ahmed
通讯作者	El-Rawy, M
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:13
英文摘要	One of the most critical stages for developing groundwater resources for drinking water use is assessing the water quality. The use of a Water Quality Index (WQI) is considered an effective method of evaluating water quality. The objective of this research was to evaluate the performance of six multiple artificial intelligence techniques, i.e., linear regression (stepwise), support vector regression SVM (linear and polynomial kernels), Gaussian process regression (GPR), Fit binary tree, and artificial neural network ANN (Bayesian) to predict the WQI in Jizan, Southern Saudi Arabia. A total of 145 groundwater samples were collected from shallow dug wells and boreholes tapping the phreatic aquifer. The WQI was calculated from 11 physicochemical parameters (pH, TDS, Ca2+, Mg2+, Na+, K+, Cl-, SO42-, HCO3-, NO3-, and TH). The spatial distribution results showed that higher values of Cl- and SO42- were recorded in the places close to the coastline, indicating the occurrence of seawater intrusion and salinisation. Seven wells had a WQI of greater than 300, indicating that the water was unfit for consumption. The results showed that the GPR, linear regression (stepwise), and ANN models performed best during the training and testing stages, with a high correlation of 1.00 and low errors. The stepwise fitting model indicated that pH, K+, and NO3- were the most significant variables, while HCO3- was a non-significant variable for the WQI. The GPR, stepwise regression, and ANN models performed best during the training and testing stages, with a high correlation and low errors. In contrast, the SVM and Fit binary tree models performed the worst in the training and testing phases. Based on subset regression analysis, the optimum input combination for WQI model prediction was determined as these eight input combinations with high R-2 (0.975-1.00) and high Adj-R-2 (0.974-1.00). The resultant WQI model significantly contributes to sustainable groundwater resource management in arid areas and generates improved prediction precision with fewer input parameters.
英文关键词	water quality index artificial intelligence support vector machine Gaussian process regression stepwise regression
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:001028228100001
WOS关键词	WATER-QUALITY ; COASTAL AQUIFERS ; INDEX ; INTRUSION ; HYDROGEOCHEMISTRY ; MODEL ; WEST
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/2XILL650/399045
推荐引用方式 GB/T 7714	El-Rawy, Mustafa,Batelaan, Okke,Alshehri, Fahad,et al. An Integrated GIS and Machine-Learning Technique for Groundwater Quality Assessment and Prediction in Southern Saudi Arabia[J],2023,15(13).
APA	El-Rawy, Mustafa,Batelaan, Okke,Alshehri, Fahad,Almadani, Sattam,Ahmed, Mohamed S.,&Elbeltagi, Ahmed.(2023).An Integrated GIS and Machine-Learning Technique for Groundwater Quality Assessment and Prediction in Southern Saudi Arabia.WATER,15(13).
MLA	El-Rawy, Mustafa,et al."An Integrated GIS and Machine-Learning Technique for Groundwater Quality Assessment and Prediction in Southern Saudi Arabia".WATER 15.13(2023).