干旱区生态环境知识资源中心(Arid): Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

Arid

DOI	10.3390/su15129687
	Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments
	Derdour, Abdessamed; Abdo, Hazem Ghassan; Almohamad, Hussein; Alodah, Abdullah; Al Dughairi, Ahmed Abdullah; Ghoneim, Sherif S. M.; Ali, Enas
通讯作者	Alodah, A
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2023
卷号	15 期号:12
英文摘要	Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab's classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO3), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management.
英文关键词	ground water water quality IWQI artificial intelligence support vector machine k-nearest neighbors environment
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:001015859100001
WOS关键词	IRRIGATION
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398804
推荐引用方式 GB/T 7714	Derdour, Abdessamed,Abdo, Hazem Ghassan,Almohamad, Hussein,et al. Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments[J],2023,15(12).
APA	Derdour, Abdessamed.,Abdo, Hazem Ghassan.,Almohamad, Hussein.,Alodah, Abdullah.,Al Dughairi, Ahmed Abdullah.,...&Ali, Enas.(2023).Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments.SUSTAINABILITY,15(12).
MLA	Derdour, Abdessamed,et al."Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments".SUSTAINABILITY 15.12(2023).