干旱区生态环境知识资源中心(Arid): Simulation of Quantity and Quality of Saq Aquifer Using Artificial Intelligence and Hydraulic Models

Arid

DOI	10.1155/2022/5910989
	Simulation of Quantity and Quality of Saq Aquifer Using Artificial Intelligence and Hydraulic Models
	Ghumman, Abdul Razzaq; Pasha, Ghufran Ahmed; Shafiquzzaman, Md.; Ahmad, Afaq; Ahmed, Afzal; Khan, Riaz Akhtar; Farooq, Rashid
通讯作者	Ahmad, A
来源期刊	ADVANCES IN CIVIL ENGINEERING
ISSN	1687-8086
EISSN	1687-8094
出版年	2022
卷号	2022
英文摘要	Scarcity of water resources is becoming a threatening issue in arid regions like Gulf. Accurate prediction of quantities and quality of groundwater is the first step towards better management of water resources where groundwater is the major source of water supply. Groundwater modelling with respect to its quantity and quality has been performed in this paper using Artificial Neural Networks (ANNs), Adaptive Neurofuzzy Inference System (ANFIS), and hydraulic model MODFLOW. Five types of ANN models with various training functions have been investigated to find the most efficient training function for the prediction of quantity and quality of groundwater, which is an original contribution useful for engineering sector. The results of the hydraulic model, ANFIS, and ANN have been compared. Nash-Sutcliffe Model Efficiency and Mean Square Error have been used for assessing the performance of models. Taylor's Diagram has also been used to compare various models. The part of Saq Aquifer lying in the Qassim Region has been investigated as the study area. Modern tools, including Geographical Information System (GIS) and Digital Elevation Model (DEM) are applied to process the required data for modelling. Climatic, geographical, and quality of groundwater (contaminants) data are obtained from the Ministry of Environment, Water, and Agriculture, Jeddah/Riyadh. ANFIS model is found to be the most efficient for modelling both the quality and quantity of the aquifer. Sensitivity analysis was performed, and then various future scenarios were investigated for sustainable groundwater pumping. The results of the research will be useful for the community and experts working in the field of water resources engineering, planning, and management in arid regions.
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:000805165400001
WOS关键词	GROUNDWATER LEVEL ; NEURAL-NETWORK ; PREDICTION ; OPTIMIZATION ; SYSTEM ; BASIN ; FLOW
WOS类目	Construction & Building Technology ; Engineering, Civil
WOS研究方向	Construction & Building Technology ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/391544
推荐引用方式 GB/T 7714	Ghumman, Abdul Razzaq,Pasha, Ghufran Ahmed,Shafiquzzaman, Md.,et al. Simulation of Quantity and Quality of Saq Aquifer Using Artificial Intelligence and Hydraulic Models[J],2022,2022.
APA	Ghumman, Abdul Razzaq.,Pasha, Ghufran Ahmed.,Shafiquzzaman, Md..,Ahmad, Afaq.,Ahmed, Afzal.,...&Farooq, Rashid.(2022).Simulation of Quantity and Quality of Saq Aquifer Using Artificial Intelligence and Hydraulic Models.ADVANCES IN CIVIL ENGINEERING,2022.
MLA	Ghumman, Abdul Razzaq,et al."Simulation of Quantity and Quality of Saq Aquifer Using Artificial Intelligence and Hydraulic Models".ADVANCES IN CIVIL ENGINEERING 2022(2022).