Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg-Marquardt method in Elazig, Turkey

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dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.contributor.authorKartal, Veysi
dc.date.accessioned2024-12-24T19:24:51Z
dc.date.available2024-12-24T19:24:51Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractThe phenomenon of evapotranspiration (ET) is closely linked to the issue of water scarcity, as it involves water loss through both evaporation and plant transpiration. Accurate prediction of evapotranspiration is of utmost importance in the strategic planning of agricultural irrigation, effective management of water resources, and precise hydrological modeling. The current investigation aims to predict the monthly ET values in the Elazig province by developing an artificial neural network (ANN) model utilizing the Levenberg-Marquardt method. Consequently, the values of temperature, precipitation, relative humidity, solar hour, and mean wind speed were utilized in forecasting evapotranspiration values by implementing ANN algorithms. This research makes a valuable contribution to the existing body of literature by utilizing an ANN model developed with the Levenberg-Marquardt method to estimate evapotranspiration. It has been discovered that evapotranspiration values are impacted by various factors such as temperature (minimum, average, maximum), relative humidity (minimum, average, maximum), wind speed, solar hour, and precipitation values, which are taken into consideration for prediction. The findings indicated that Elazig, Keban, Baskil, and Agin sites had R values of 0.9995, 0.9948, 0.9898, and 0.9994 in the proposed model. It was found that Elazig's MAPE ranged from 0 to 0.2288, Keban's was 0.0001 to 0.3703, Baskil's was between 0 and 0.4453, and Agin's was both 0 and 0.2784. The findings obtained from the proposed model are compatible with evapotranspiration values computed from the Hargreaves method (R2 = 0.996). The study's findings provide significant insights for planners and decision-makers involved in the planning and managing water resources and agricultural irrigation.
dc.description.sponsorshipSiirt University
dc.description.sponsorshipSpecial thanks to the General Directorate of Meteorology (MGM) for providing the database used in this study.
dc.identifier.doi10.1007/s11356-024-32464-1
dc.identifier.issn0944-1344
dc.identifier.issn1614-7499
dc.identifier.pmid38381292
dc.identifier.scopus2-s2.0-85185473939
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s11356-024-32464-1
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6150
dc.identifier.wosWOS:001171864200009
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofEnvironmental Science and Pollution Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectEstimation
dc.subjectEvapotranspiration
dc.subjectANN
dc.subjectLevenberg-Marquardt
dc.subjectMachine learning
dc.titlePrediction of monthly evapotranspiration by artificial neural network model development with Levenberg-Marquardt method in Elazig, Turkey
dc.typeArticle

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