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Empirical Penman-Monteith equation and artificial intelligence techniques in predicting reference evapotranspiration: A review

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dc.citedby9
dc.contributor.authorAbdullah S.S.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorid57213171981en_US
dc.contributor.authorid55636320055en_US
dc.date.accessioned2023-05-29T06:14:01Z
dc.date.available2023-05-29T06:14:01Z
dc.date.issued2016
dc.descriptionAgricultural robots; Evapotranspiration; Neural networks; Water supply; Weather forecasting; Artificial intelligence techniques; Extreme learning machine; Fao penman monteiths; Food and agriculture organizations; Irrigation projects; Penman-Monteith equations; Reference evapotranspiration; Weather parameters; Learning systems; artificial intelligence; artificial neural network; climate conditions; empirical analysis; evapotranspiration; Food and Agricultural Organization; machine learning; Penman-Monteith equation; prediction; United Nationsen_US
dc.description.abstractEvapotranspiration is a fundamental requirement in the planning and management of irrigation projects. Methods of predicting evapotranspiration (ET) are numerous, but the Food and Agriculture Organization (FAO) of the United Nations adopted the FAO Penman-Monteith (PM) equation, as the method which provides the most accurate results for the prediction of reference evapotranspiration (ET0) in all regions and for all weather conditions. The main identified drawback in the application of this method is the wide variety of weather parameters required for the prediction. To overcome this problem, artificial neural networks (ANNs) models have been proposed to simulate the nonlinear, dynamic ET0 processes. This paper highlights both the traditional empirical PM method, and the enhancement obtained by the utilisation of ANN techniques in predicting ET0. � 2016 Inderscience Enterprises Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1504/IJW.2016.073741
dc.identifier.epage66
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84953339870
dc.identifier.spage55
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84953339870&doi=10.1504%2fIJW.2016.073741&partnerID=40&md5=69ecfe4c19a9417cb6c485bd92c53a61
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22998
dc.identifier.volume10
dc.publisherInderscience Publishersen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Water
dc.titleEmpirical Penman-Monteith equation and artificial intelligence techniques in predicting reference evapotranspiration: A reviewen_US
dc.typeReviewen_US
dspace.entity.typePublication
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