干旱区生态环境知识资源中心(Arid): Forecasting of Groundwater Quality by Using Deep Learning Time Series Techniques in an Arid Region

Arid

DOI	10.3390/su15086529
	Forecasting of Groundwater Quality by Using Deep Learning Time Series Techniques in an Arid Region
	Ahmed, Ahmed Khaled Abdella; El-Rawy, Mustafa; Ibraheem, Amira Mofreh; Al-Arifi, Nassir; Abd-Ellah, Mahmoud Khaled
通讯作者	El-Rawy, M
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2023
卷号	15 期号:8
英文摘要	Groundwater is regarded as the primary source of agricultural and drinking water in semi-arid and arid regions. However, toxic substances released from sources such as landfills, industries, insecticides, and fertilizers from the previous year exhibited extreme levels of groundwater contamination. As a result, it is crucial to assess the quality of the groundwater for agricultural and drinking activities, both its current use and its potential to become a reliable water supply for individuals. The quality of the groundwater is critical in Egypt's Sohag region because it serves as a major alternative source of agricultural activities and residential supplies, in addition to providing drinking water, and residents there frequently have issues with the water's suitability for human consumption. This research assesses groundwater quality and future forecasting using Deep Learning Time Series Techniques (DLTS) and long short-term memory (LSTM) in Sohag, Egypt. Ten groundwater quality parameters (pH, Sulfate, Nitrates, Magnesium, Chlorides, Iron, Total Coliform, TDS, Total Hardness, and Turbidity) at the seven pumping wells were used in the analysis to create the water quality index (WQI). The model was tested and trained using actual data over nine years from seven wells in Sohag, Egypt. The high quantities of iron and magnesium in the groundwater samples produced a high WQI. The proposed forecasting model provided good performances in terms of average mean-square error (MSE) and average root-mean-square error (RMSE) with values of 1.6091 x 10(-7) and 4.0114 x 10(-4), respectively. The WQI model's findings demonstrated that it could assist managers and policymakers in better managing groundwater resources in arid areas.
英文关键词	water quality index (WQI) deep learning time series forecasting Sohag Egypt
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:000979403300001
WOS关键词	NILE VALLEY ; WATER ; GOVERNORATE ; HYDROGEOCHEMISTRY ; AQUIFER ; INDEX ; AREAS ; PART ; GIS
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/2XILL650/398782
推荐引用方式 GB/T 7714	Ahmed, Ahmed Khaled Abdella,El-Rawy, Mustafa,Ibraheem, Amira Mofreh,et al. Forecasting of Groundwater Quality by Using Deep Learning Time Series Techniques in an Arid Region[J],2023,15(8).
APA	Ahmed, Ahmed Khaled Abdella,El-Rawy, Mustafa,Ibraheem, Amira Mofreh,Al-Arifi, Nassir,&Abd-Ellah, Mahmoud Khaled.(2023).Forecasting of Groundwater Quality by Using Deep Learning Time Series Techniques in an Arid Region.SUSTAINABILITY,15(8).
MLA	Ahmed, Ahmed Khaled Abdella,et al."Forecasting of Groundwater Quality by Using Deep Learning Time Series Techniques in an Arid Region".SUSTAINABILITY 15.8(2023).