Assessing rainfall prediction models: Artificial neural networks (ANN)

but.event.date15.05.2025cs
but.event.titleJuniorstav 2025cs
dc.contributor.authorYildiz, Halil Ibrahim
dc.date.accessioned2025-12-05T12:14:06Z
dc.date.issued2025-09-30cs
dc.description.abstractPrecipitation data are important for solving engineering problems. Missing data can be predicted, and historical data can be used to construct precipitation models. This study used monthly precipitation data, temperature, relative humidity, wind, and evaporation data from the Afyon Meteorological Observatory station between 1929 and 2018. The ANN (Artificial Neural Network) method was used to predict precipitation data, and the results were compared with MLR (Multilinear Regression) models.en
dc.formattextcs
dc.format.extent1-10cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationJuniorstav 2025: Proceedings of the 27th International Scientific Conference Of Civil Engineering, s. 1-10. ISBN 978-80-86433-88-2.cs
dc.identifier.doi10.13164/juniorstav.2025.25047en
dc.identifier.isbn978-80-86433-88-2en
dc.identifier.urihttps://hdl.handle.net/11012/255659
dc.language.isoencs
dc.publisherVysoké učení technické v Brně,Fakulta stavebnícs
dc.relation.ispartofJuniorstav 2025: Proceedings of the 27th International Scientific Conference Of Civil Engineeringcs
dc.relation.urihttps://juniorstav.fce.vutbr.cz/proceedings2025/
dc.rights© Vysoké učení technické v Brně,Fakulta stavebnícs
dc.rights.accessopenAccessen
dc.subjectArtificial Neural Networksen
dc.subjectMultilinear Regressionen
dc.subjectPrecipitationen
dc.subjectHydrologyen
dc.titleAssessing rainfall prediction models: Artificial neural networks (ANN)en
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta stavebnícs

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