干旱区生态环境知识资源中心(Arid): Modeling groundwater quality by using hybrid intelligent and geostatistical methods

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DOI	10.1007/s11356-020-09188-z
	Modeling groundwater quality by using hybrid intelligent and geostatistical methods
	Maroufpoor, Saman; Jalali, Mohammadnabi; Nikmehr, Saman; Shiri, Naser; Shiri, Jalal; Maroufpoor, Eisa
通讯作者	Maroufpoor, E
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2020
卷号	27 期号:22 页码:28183-28197
英文摘要	Simulation of groundwater quality is important for managing water resources and mitigating water shortages, especially in arid and semiarid areas. Geostatistical models have been used for spatial prediction and interpolation of groundwater parameters. Recently, hybrid intelligent models have been employed for the simulation of dynamic systems. In this study, hybrid intelligent models, based on a neuro-fuzzy system integrated with fuzzy c-means data clustering (FCM) and grid partition (GP) models as well as artificial neural networks integrated with particle swarm optimization algorithm, were used to predict the spatial distribution of chlorine (Cl), electrical conductivity (EC), and sodium absorption ratio (SAR) parameters of groundwater. Results of the hybrid models were compared with geostatistical methods, including kriging, inverse distance weighting (IDW), and radial basis function (RBF). The latitude and longitude values of observation wells and qualitative parameters in three states of maximum, average, and minimum were introduced as input and output to the models, respectively. To evaluate the models, the root mean squared error (RMSE), the mean absolute error (MAE), and CC statistical criteria were used. Results showed that in the hybrid models, NF-GP with the lowest RMSE and MAE and highest CC was the most suitable model for the prediction of water quality parameters. The RMSE, MAE, and CC values were 107.175 (mg/L), 79.804 (mg/L), and 0.924 in the average state for Cl; were 518.544 (mu mho/cm), 444.152 (mu mho/cm), and 0.882 for electrical conductivity; and were 1.596, 1.350, and 0.582 for sodium absorption ratio, respectively. Among the geostatistical models, the kriging was found more accurate. Using the coordinates of wells will eventually allow the NF-GP to be used for more sampling and replace the visual techniques that require more time, cost, and facilities.
英文关键词	Groundwater quality Geographic information Geostatistics Neuro-fuzzy system Artificial neural network
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000533138700008
WOS关键词	DATA-DRIVEN METHODS ; ARTIFICIAL NEURAL-NETWORKS ; SPATIAL VARIABILITY ; RISK-ASSESSMENT ; FUZZY MODELS ; WATER ; EVAPOTRANSPIRATION ; OPTIMIZATION ; PREDICTION ; PSO
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/2XILL650/324456
作者单位	[Maroufpoor, Saman] Univ Tehran, Dept Irrigat & Reclamat Engn, Tehran, Iran; [Jalali, Mohammadnabi] Islamic Azad Univ, Dept Water Sci & Engn, Sci & Res Branch, Tehran, Iran; [Nikmehr, Saman; Maroufpoor, Eisa] Univ Kurdistan, Fac Agr, Dept Water Engn, Sanandaj, Iran; [Shiri, Naser] Univ Tabriz, Dept Civil Engn, Tabriz, Iran; [Shiri, Jalal] Univ Tabriz, Fac Agr, Water Engn Dept, Tabriz, Iran; [Shiri, Jalal] Univ Tabriz, Fac Civil Engn, Ctr Excellence Hydroinformat, Tabriz, Iran
推荐引用方式 GB/T 7714	Maroufpoor, Saman,Jalali, Mohammadnabi,Nikmehr, Saman,et al. Modeling groundwater quality by using hybrid intelligent and geostatistical methods[J],2020,27(22):28183-28197.
APA	Maroufpoor, Saman,Jalali, Mohammadnabi,Nikmehr, Saman,Shiri, Naser,Shiri, Jalal,&Maroufpoor, Eisa.(2020).Modeling groundwater quality by using hybrid intelligent and geostatistical methods.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,27(22),28183-28197.
MLA	Maroufpoor, Saman,et al."Modeling groundwater quality by using hybrid intelligent and geostatistical methods".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 27.22(2020):28183-28197.