Precipitation forecast with artificial neural networks method

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dc.contributor.authorAnsay, Serkan
dc.contributor.authorKöse, Bayram
dc.date.accessioned2025-03-21T07:38:14Z
dc.date.available2025-03-21T07:38:14Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractEvents in the atmosphere from past to present – wind, precipitation, humidity, temperature – have almost always been the subject of research to create a forecast in regions. The rapid development of the technological field in terms of software and hardware brings methods and techniques to be used in research. One of them is Artificial Neural Networks. In this study, precipitation data were estimated using the Feed Forward Backpropagation method of Artificial Neural Networks method using past data of meteorological parameters, and they were compared with the data of multiple linear regression analysis. Based on these models, six different models were studied, and regression and performance evaluations were made. While the error average of multiple linear regression is 0.2413, this value is 0.076 in artificial neural networks, and the correlation average for both is 0.90. As a result of this study, the best model has a coefficient of determination of 0.95 and an error value of 0.18 in multiple linear regression, as well as a coefficient of certainty of 0.99 and an error value of 0.0438 in artificial neural networks; It has been understood that the 1st model, which has 6 data sets as the input layer, exhibits the best performance.
dc.description.sponsorshipİzmir Academy Association
dc.identifier.doi10.61969/jai.1310918
dc.identifier.endpage31
dc.identifier.issn3023-4018
dc.identifier.issue1
dc.identifier.startpage15
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2730
dc.identifier.urihttps://doi.org/10.61969/jai.1310918
dc.identifier.volume7
dc.language.isoen
dc.publisherİzmir Akademi Derneği
dc.relation.ispartofJournal of AI
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20250319
dc.subjectRegression
dc.subjectartificial neural networks
dc.subjectprecipitation forecast
dc.titlePrecipitation forecast with artificial neural networks method
dc.typeArticle

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