Application of artificial neural networks to the design of subsurface drainage systems in Libyan agricultural projects

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dc.contributor.authorEllafi, Murad A.
dc.contributor.authorDeeks, Lynda K.
dc.contributor.authorSimmons, Robert W.
dc.date.accessioned2021-06-04T11:49:41Z
dc.date.available2021-06-04T11:49:41Z
dc.date.issued2021-05-18
dc.description.abstractStudy 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.en_UK
dc.identifier.citationEllafi MA, Deeks LK, Simmons RW. (2021) Application of artificial neural networks to the design of subsurface drainage systems in Libyan agricultural projects. Journal of Hydrology: Regional Studies, Volume 35, June 2021, Article number 100832en_UK
dc.identifier.issn2214-5818
dc.identifier.urihttps://doi.org/10.1016/j.ejrh.2021.100832
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16736
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectArid areasen_UK
dc.subjectSub-surface drainageen_UK
dc.subjectPedotransfer functionsen_UK
dc.subjectAgricultural drainage designen_UK
dc.subjectArtificial neural networksen_UK
dc.subjectSaturated hydraulic conductivityen_UK
dc.titleApplication of artificial neural networks to the design of subsurface drainage systems in Libyan agricultural projectsen_UK
dc.typeArticleen_UK

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