Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81366
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dc.contributor.authorJing, Wen_US
dc.contributor.authorYaseen, ZMen_US
dc.contributor.authorShahid, Sen_US
dc.contributor.authorSaggi, MKen_US
dc.contributor.authorTao, Hen_US
dc.contributor.authorKisi, Oen_US
dc.contributor.authorSalih, SQen_US
dc.contributor.authorAl-Ansari, Nen_US
dc.contributor.authorChau, KWen_US
dc.date.accessioned2019-09-20T00:55:12Z-
dc.date.available2019-09-20T00:55:12Z-
dc.date.issued2019-
dc.identifier.citationEngineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 811-823en_US
dc.identifier.issn1994-2060en_US
dc.identifier.urihttp://hdl.handle.net/10397/81366-
dc.description.abstractEvapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ETo) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ETo is a major concern in hydrology. ETo can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ETo. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ETo. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ETo. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ETo in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ETo using EC.en_US
dc.description.sponsorshipDepartment of Civil and Environmental Engineeringen_US
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.relation.ispartofEngineering applications of computational fluid mechanicsen_US
dc.rights© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Groupen_US
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Wang Jing, Zaher Mundher Yaseen, Shamsuddin Shahid, Mandeep Kaur Saggi, Hai Tao, Ozgur Kisi, Sinan Q. Salih, Nadhir Al-Ansari & Kwok-Wing Chau (2019) Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions, Engineering Applications of Computational Fluid Mechanics, 13:1, 811-823 is available at https://dx.doi.org/10.1080/19942060.2019.1645045en_US
dc.subjectEvapotranspiration predictionen_US
dc.subjectState of the arten_US
dc.subjectEvolutionary computing modelsen_US
dc.subjectInput variabilityen_US
dc.subjectFuture research directionsen_US
dc.titleImplementation of evolutionary computing models for reference evapotranspiration modeling : short review, assessment and possible future research directionsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage811en_US
dc.identifier.epage823en_US
dc.identifier.volume13en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/19942060.2019.1645045en_US
dc.identifier.isiWOS:000480241200001-
dc.identifier.scopus2-s2.0-85070912161-
dc.identifier.eissn1997-003Xen_US
dc.description.validate201909 bcrc-
dc.description.oapublished_final-
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