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Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration

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dc.citedby6
dc.contributor.authorAbdullah S.S.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorAbdullah N.S.en_US
dc.contributor.authorMustapha A.en_US
dc.contributor.authorid57213171981en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid56644103800en_US
dc.contributor.authorid57200530694en_US
dc.date.accessioned2023-05-29T06:00:10Z
dc.date.available2023-05-29T06:00:10Z
dc.date.issued2015
dc.descriptionartificial neural network; back propagation; evapotranspiration; genetic algorithm; resource scarcity; semiarid region; water demand; Baghdad [Iraq]; Iraqen_US
dc.description.abstractWater scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water, is one of the goals that should be adopted in order to avoid more squandering of water especially in arid and semiarid regions. The capabilities of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration (ET0) are evaluated in this paper in comparison with the empirical FAO Penman-Monteith (P-M) equation, later a model of FFBP+Genetic Algorithm (GA) is implemented for the same evaluation purpose. The study location is the main station in Iraq, namely Baghdad Station. Records of weather variables from the related meteorological station, including monthly mean records of maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh) and wind speed (U2), from the related meteorological station are used in the prediction of ET0 values. The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET0 and could be recommended for modeling of ET0 in arid and semiarid regions.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.17576/jsm-2015-4407-18
dc.identifier.epage1059
dc.identifier.issue7
dc.identifier.scopus2-s2.0-84941078614
dc.identifier.spage1053
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84941078614&doi=10.17576%2fjsm-2015-4407-18&partnerID=40&md5=d2bc6863379b6e9aa1063b8f2c801358
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22318
dc.identifier.volume44
dc.publisherPenerbit Universiti Kebangsaan Malaysiaen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleSains Malaysiana
dc.titleFeedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspirationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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