Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition

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dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.authoridElbeltagi, Ahmed/0000-0002-5506-9502
dc.authoridElsadek, Elsayed/0000-0002-8402-467X
dc.contributor.authorElbeltagi, Ahmed
dc.contributor.authorKatipoglu, Okan Mert
dc.contributor.authorKartal, Veysi
dc.contributor.authorDanandeh Mehr, Ali
dc.contributor.authorBerhail, Sabri
dc.contributor.authorElsadek, Elsayed Ahmed
dc.date.accessioned2024-12-24T19:25:04Z
dc.date.available2024-12-24T19:25:04Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractVarious critical applications, spanning from watershed management to agricultural planning and ecological sustainability, hinge upon the accurate prediction of reference evapotranspiration (ETo). In this context, our study aimed to enhance the accuracy of ETo prediction models by combining a variety of signal decomposition techniques with an Artificial Bee Colony (ABC)-artificial neural network (ANN) (codename: ABC-ANN). To this end, historical (1979-2014) daily climate variables, including maximum temperature, minimum temperature, mean temperature, wind speed, relative humidity, solar radiation, and precipitation from four arid and semi-arid regions in Egypt: Al-Qalyubiyah, Cairo, Damietta, and Port Said, were used. Six techniques, namely, Empirical Mode Decomposition, Variational Mode Decomposition, Ensemble Empirical Mode Decomposition, Local Mean Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, and Empirical Wavelet Transform were used to evaluate signal decomposition efficiency in ETo prediction. Our results showed that the highest ETo prediction accuracy was obtained with ABC-ANN (Train R-2: 0.990 and Test R-2: 0.989), (Train R-2: 0.986 and Test R-2: 0.986), (Train R-2: 0.991 and Test R-2: 0.989) and (Train R-2: 0.988 and Test R-2: 0.987) for Al-Qalyubiyah, Cairo, Damietta, and Port Said, respectively. The impressive results of our hybrid model attest to its importance as a powerful tool for tackling the problems associated with ETo prediction.
dc.description.sponsorshipMansoura University
dc.description.sponsorshipThanks to Grammarly Premium, which helps correct grammatical errors and spelling mistakes in the article.
dc.identifier.doi10.1007/s13201-024-02308-x
dc.identifier.issn2190-5487
dc.identifier.issn2190-5495
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85209794651
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s13201-024-02308-x
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6229
dc.identifier.volume14
dc.identifier.wosWOS:001352861600002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofApplied Water Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectCrop water use estimation
dc.subjectHybrid ABC-ANN
dc.subjectETo prediction
dc.subjectDatasets integration
dc.subjectArid and semi-arid regions
dc.subjectEgypt
dc.titleAdvanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition
dc.typeArticle

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