Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81781
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorYaseen, ZMen_US
dc.creatorAl-Juboori, AMen_US
dc.creatorBeyaztas, Uen_US
dc.creatorAl-Ansari, Nen_US
dc.creatorChau, KWen_US
dc.creatorQi, CCen_US
dc.creatorAli, Men_US
dc.creatorSalih, SQen_US
dc.creatorShahid, Sen_US
dc.date.accessioned2020-02-10T12:29:09Z-
dc.date.available2020-02-10T12:29:09Z-
dc.identifier.issn1994-2060en_US
dc.identifier.urihttp://hdl.handle.net/10397/81781-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rights© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_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 Yaseen, Z. M., Al-Juboori, A. M., Beyaztas, U., Al-Ansari, N., Chau, K. W., Qi, C. C., . . . Shahid, S. (2020). Prediction of evaporation in arid and semi-arid regions : a comparative study using different machine learning models. Engineering Applications of Computational Fluid Mechanics, 14(1), 70-89 is available at https://dx.doi.org/10.1080/19942060.2019.1680576en_US
dc.subjectEvaporationen_US
dc.subjectPredictive modelen_US
dc.subjectMachine learningen_US
dc.subjectArid and semi-arid regionsen_US
dc.subjectBest input combinationen_US
dc.titlePrediction of evaporation in arid and semi-arid regions : a comparative study using different machine learning modelsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage70en_US
dc.identifier.epage89en_US
dc.identifier.volume14en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/19942060.2019.1680576en_US
dcterms.abstractEvaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation - the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM) - were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R-2 = .92), and with all variables as inputs at Station II (R-2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 70-89en_US
dcterms.isPartOfEngineering applications of computational fluid mechanicsen_US
dcterms.issued2020-
dc.identifier.isiWOS:000496623500001-
dc.identifier.scopus2-s2.0-85075131156-
dc.identifier.eissn1997-003Xen_US
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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