干旱区生态环境知识资源中心(Arid): Application of artificial neural networks to the design of subsurface drainage systems in Libyan agricultural projects

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DOI	10.1016/j.ejrh.2021.100832
	Application of artificial neural networks to the design of subsurface drainage systems in Libyan agricultural projects
	Ellafi, Murad A.; Deeks, Lynda K.; Simmons, Robert W.
通讯作者	Deeks, LK (corresponding author), Cranfield Univ, Cranfield Soil & Agrifood Inst, Bldg 52a, Cranfield MK43 0AL, Beds, England.
来源期刊	JOURNAL OF HYDROLOGY-REGIONAL STUDIES
EISSN	2214-5818
出版年	2021
卷号	35
英文摘要	Study region: The study data draws on the drainage design for Hammam agricultural project (HAP) and Eshkeda agricultural project (EAP), located in the south of Libya, north of the Sahara Desert. The results of this study are applicable to other arid areas. Study focus: This study aims to improve the prediction of saturated hydraulic conductivity (Ksat) to enhance the efficacy of drainage system design in data-poor areas. Artificial Neural Networks (ANNs) were developed to estimate Ksat and compared with empirical regression-type Pedotransfer Function (PTF) equations. Subsequently, the ANNs and PTFs estimated Ksat values were used in EnDrain software to design subsurface drainage systems which were evaluated against designs using measured Ksat values. New hydrological insights: Results showed that ANNs more accurately predicted Ksat than PTFs. Drainage design based on PTFs predictions (1) result in a deeper water-level and (2) higher drainage density, increasing costs. Drainage designs based on ANNs predictions gave drain spacing and water table depth equivalent to those predicted using measured data. The results of this study indicate that ANNs can be developed using existing and under-utilised data sets and applied successfully to data-poor areas. As Ksat is time-consuming to measure, basing drainage designs on ANN predictions generated from alternative datasets will reduce the overall cost of drainage designs making them more accessible to farmers, planners, and decision-makers in least developed countries.
英文关键词	Saturated hydraulic conductivity Artificial neural networks Agricultural drainage design Pedotransfer functions Sub-surface drainage Arid areas
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000663478500005
WOS关键词	SATURATED HYDRAULIC CONDUCTIVITY ; SOIL-WATER RETENTION ; PEDOTRANSFER FUNCTIONS ; VARIABILITY ; PREDICTION ; REGRESSION ; PATTERNS ; CURVE
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350915
作者单位	[Ellafi, Murad A.; Deeks, Lynda K.; Simmons, Robert W.] Cranfield Univ, Cranfield Soil & Agrifood Inst, Bldg 52a, Cranfield MK43 0AL, Beds, England; [Ellafi, Murad A.] Univ Tripoli, Dept Soil & Water Sci, Tripoli 13538, Libya
推荐引用方式 GB/T 7714	Ellafi, Murad A.,Deeks, Lynda K.,Simmons, Robert W.. Application of artificial neural networks to the design of subsurface drainage systems in Libyan agricultural projects[J],2021,35.
APA	Ellafi, Murad A.,Deeks, Lynda K.,&Simmons, Robert W..(2021).Application of artificial neural networks to the design of subsurface drainage systems in Libyan agricultural projects.JOURNAL OF HYDROLOGY-REGIONAL STUDIES,35.
MLA	Ellafi, Murad A.,et al."Application of artificial neural networks to the design of subsurface drainage systems in Libyan agricultural projects".JOURNAL OF HYDROLOGY-REGIONAL STUDIES 35(2021).