Publication:
RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region

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dc.citedby13
dc.contributor.authorKamel A.H.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57210233114en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:07:31Z
dc.date.available2023-05-29T09:07:31Z
dc.date.issued2021
dc.descriptionArid regions; Forecasting; Gravel; Radial basis function networks; Reservoirs (water); Soils; Stochastic systems; Wind; Accurate prediction; Correlation coefficient; Generalized Regression Neural Network(GRNN); Nonlinear process; Prediction accuracy; Radial basis function neural networks; Statistical indicators; Subsurface reservoir; Evaporationen_US
dc.description.abstractEvaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir� water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model's input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively. � 2021 Elsevier Inc.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo100514
dc.identifier.doi10.1016/j.suscom.2021.100514
dc.identifier.scopus2-s2.0-85100111551
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85100111551&doi=10.1016%2fj.suscom.2021.100514&partnerID=40&md5=9dff01f3ec5428c0ce6b7a7e1c7046c7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26184
dc.identifier.volume30
dc.publisherElsevier Inc.en_US
dc.sourceScopus
dc.sourcetitleSustainable Computing: Informatics and Systems
dc.titleRBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid regionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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