干旱区生态环境知识资源中心(Arid): Integrated machine learning-based model and WQI for groundwater quality assessment: ML, geospatial, and hydro-index approaches

Arid

DOI	10.1007/s11356-023-25938-1
	Integrated machine learning-based model and WQI for groundwater quality assessment: ML, geospatial, and hydro-index approaches
	Abu El-Magd, Sherif Ahmed; Ismael, Ismael S.; El-Sabri, Mohamed A. Sh.; Abdo, Mohamed Sayed; Farhat, Hassan I.
通讯作者	Abu El-Magd, SA
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2023
卷号	30 期号:18 页码:53862-53875
英文摘要	The demands upon the arid area for water supply pose threats to both the quantity and quality of social and economic activities. Thus, a widely used machine learning model, namely the support vector machines (SVM) integrated with water quality indices (WQI), was used to assess the groundwater quality. The predictive ability of the SVM model was assessed using a field dataset for groundwater from Abu-Sweir and Abu-Hammad, Ismalia, Egypt. Multiple water quality parameters were chosen as independent variables to build the model. The results revealed that the permissible and unsuitable class values range from 36 to 27%, 45 to 36%, and 68 to 15% for the WQI approach, SVM method and SVM-WQI model respectively. Besides, the SVM-WQI model shows a low percentage of the area for excellent class compared to the SVM model and WQI. The SVM model trained with all predictors with a mean square error (MSE) of 0.002 and 0.41; the models that had higher accuracy reached 0.88. Moreover, the study highlighted that SVM-WQI can be successfully implemented for the assessment of groundwater quality (0.90 accuracy). The resulting groundwater model in the study sites indicates that the groundwater is influenced by rock-water interaction and the effect of leaching and dissolution. Overall, the integrated ML model and WQI give an understanding of water quality assessment, which may be helpful in the future development of such areas.
英文关键词	Machine learning model (ML) SVM WQI SVM-WQI Egypt
类型	Article
语种	英语
开放获取类型	hybrid, Green Published
收录类别	SCI-E
WOS记录号	WOS:000943012900002
WOS关键词	WATER-QUALITY ; SUITABILITY ; PREDICTION ; PURPOSES ; DRINKING ; REGION ; INDIA
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396263
推荐引用方式 GB/T 7714	Abu El-Magd, Sherif Ahmed,Ismael, Ismael S.,El-Sabri, Mohamed A. Sh.,et al. Integrated machine learning-based model and WQI for groundwater quality assessment: ML, geospatial, and hydro-index approaches[J],2023,30(18):53862-53875.
APA	Abu El-Magd, Sherif Ahmed,Ismael, Ismael S.,El-Sabri, Mohamed A. Sh.,Abdo, Mohamed Sayed,&Farhat, Hassan I..(2023).Integrated machine learning-based model and WQI for groundwater quality assessment: ML, geospatial, and hydro-index approaches.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,30(18),53862-53875.
MLA	Abu El-Magd, Sherif Ahmed,et al."Integrated machine learning-based model and WQI for groundwater quality assessment: ML, geospatial, and hydro-index approaches".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 30.18(2023):53862-53875.