干旱区生态环境知识资源中心(Arid): Reliability evaluation of groundwater quality index using data-driven models

Arid

DOI	10.1007/s11356-021-16158-6
	Reliability evaluation of groundwater quality index using data-driven models
	Najafzadeh, Mohammad; Homaei, Farshad; Mohamadi, Sedigheh
通讯作者	Najafzadeh, M (corresponding author), Grad Univ Adv Technol, Fac Civil & Surveying Engn, Dept Water Engn, Kerman, Iran.
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2021-09
英文摘要	A trustworthy evaluation of the groundwater quality situations for different usages (i.e., drinking, industry, and agriculture) can definitely improve the management of groundwater resources for quality and quantity control, particularly in the arid and semi-arid districts. In the present investigation, GQI values and their typical categories have been yielded by the World Health Organization (WHO) instruction for the Rafsanjan Plain, the central part of Iran, during a 15-year period beginning in 2002. In this study, four robust Data-Driven Techniques (DDTs) based on the evolutionary algorithms and classification concepts have been applied to present formulations for the prediction of groundwater quality index (GQI) values in the case study of Rafsanjan Plain. In this way, monthly groundwater quality parameters (i.e., electrical conductivity, total hardness, total dissolved solid, pH, chloride, bicarbonate, sulfate, phosphate, calcium, magnesium, potassium, and sodium) were taken from 1349 observations. Performance of DDTs indicated that the Evolutionary Polynomial Regression (EPR) demonstrated the most accurate predictions of GQI than a model tree (MT), gene-expression programming (GEP), and Multivariate Adaptive Regression Spline (MARS). Moreover, to investigate all probable uncertainty in the values of groundwater quality parameters for the Rafsanjan Plain, a reliability-based probabilistic model was designed to assess the values of GQI. Hence, the Monte-Carlo scenario sampling technique has been quantified to evaluate the limit state function from DDTs. Moreover, there is a high probability (almost 100%) for the whole region to pass the Excellent quality, but it reduces to almost 50% over the Good and leads to almost 0% for the Poor quality.
英文关键词	Groundwater resources Artificial intelligence models Drinking water suitability Monte-Carlo analysis Probabilistic water quality modeling
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000693525800002
WOS关键词	DRINKING ; PURPOSES
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/2XILL650/367579
作者单位	[Najafzadeh, Mohammad] Grad Univ Adv Technol, Fac Civil & Surveying Engn, Dept Water Engn, Kerman, Iran; [Homaei, Farshad] Grad Univ Adv Technol, Fac Civil & Surveying Engn, Dept Earthquake & Geotech Engn, Kerman, Iran; [Mohamadi, Sedigheh] Grad Univ Adv Technol, Inst Environm Sci, Dept Ecol, Kerman, Iran
推荐引用方式 GB/T 7714	Najafzadeh, Mohammad,Homaei, Farshad,Mohamadi, Sedigheh. Reliability evaluation of groundwater quality index using data-driven models[J],2021.
APA	Najafzadeh, Mohammad,Homaei, Farshad,&Mohamadi, Sedigheh.(2021).Reliability evaluation of groundwater quality index using data-driven models.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH.
MLA	Najafzadeh, Mohammad,et al."Reliability evaluation of groundwater quality index using data-driven models".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2021).