Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt

doi:10.1007/s11356-020-10543-3

	Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt
	Abdel-Fattah, Mohamed K.2; Mokhtar, Ali 1,3; Abdo, Ahmed, I 2,4
刊名	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
	2020-08-21
页码	17
关键词	Water resources IWQI Artificial neural networks (ANN) ARIMA Time series Egypt
ISSN号	0944-1344
DOI	10.1007/s11356-020-10543-3
通讯作者	Abdel-Fattah, Mohamed K.(mohammedkamal8@yahoo.com) ; Mokhtar, Ali(ali.mokhtar@agr.cu.edu.eg)
英文摘要	Limited water resources are one of the major challenges facing Egypt during the current stage. The agricultural drainage water is an important water resource which can be reused for agriculture. Thus, the current study aims to assess the quality of drainage water for irrigation purpose through monitoring and predicting its suitability for irrigation. The chemical composition of Bahr El-Baqr water drain, especially salinity, as well as ions are mainly involved in calculating indicators of water suitability for irrigation, i.e., Ca2+, Mg2+, Na+, K+, HCO-3, Cl-, and SO42-. Further analysis was carried out to evaluate the irrigation water quality index (IWQI) through integrated approaches and artificial neural network (ANN) model. Further, ARIMA models were developed to forecast IWQI of Bahr El-Baqr drain in Egypt. The results indicated that the computed IWQI values ranged between 46 and 81. Around 11% of the samples were classified as excellent water, while 89% of the samples were categorized as good water. The results of IWQI showed a standard deviation of 8.59 with a mean of 62.25, indicating that IWQI varied by 13.79% from the average. ANN model showed much higher prediction accuracy in IWQI modeling withR(2)value greater than 0.98 during training, testing and validation. A relatively good correlation was obtained, between the actual and forecasted IWQI based on the Akaike information criterion (AIC); the best fit models were ARIMA (1,0) (0,0) without seasonality. The determination coefficient (R-2) of ARIMA models was 0.23. Accordingly, 23% of IWQI variability could be explained by different model parameters. These findings will support the water resources managers and decision-makers to manage the irrigation water resources that can be implemented in the future.
WOS关键词	DIFFUSE SOLAR-RADIATION ; DISSOLVED-OXYGEN ; SUNSHINE DURATION ; RIVER-BASIN ; LAND-COVER ; INDEX WQI ; PREDICTION ; REGRESSION ; INDICATORS ; RESOURCES
WOS研究方向	Environmental Sciences & Ecology
语种	英语
出版者	SPRINGER HEIDELBERG
WOS记录号	WOS:000561514500003
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/157978]
专题	中国科学院地理科学与资源研究所
通讯作者	Abdel-Fattah, Mohamed K.; Mokhtar, Ali
作者单位	1.Cairo Univ, Fac Agr, Dept Agr Engn, Giza 12613, Egypt 2.Zagazig Univ, Fac Agr, Soil Sci Dept, Zagazig 44511, Egypt 3.Northwest Agr & Forestry Univ, Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China 4.Northwest A&F Univ, Coll Nat Resources & Environm, Yangling 712100, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Abdel-Fattah, Mohamed K.,Mokhtar, Ali,Abdo, Ahmed, I. Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt[J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,2020:17.
APA	Abdel-Fattah, Mohamed K.,Mokhtar, Ali,&Abdo, Ahmed, I.(2020).Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,17.
MLA	Abdel-Fattah, Mohamed K.,et al."Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2020):17.