Precipitable water modelling using artificial neural network in Cukurova region

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dc.authoridsahin, mehmet/0000-0001-7942-9253
dc.contributor.authorSenkal, Ozan
dc.contributor.authorYildiz, B. Yigit
dc.contributor.authorSahin, Mehmet
dc.contributor.authorPestemalci, Vedat
dc.date.accessioned2024-12-24T19:24:36Z
dc.date.available2024-12-24T19:24:36Z
dc.date.issued2012
dc.departmentSiirt Üniversitesi
dc.description.abstractPrecipitable water (PW) is an important atmospheric variable for climate system calculation. Local monthly mean PW values were measured by daily radiosonde observations for the time period from 1990 to 2006. Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water data in Cukurova region, south of Turkey. We applied Levenberg-Marquardt (LM) learning algorithm and logistic sigmoid transfer function in the network. In order to train our neural network we used data of Adana station, which are assumed to give a general idea about the precipitable water of Cukurova region. Thus, meteorological and geographical data (altitude, temperature, pressure, and humidity) were used in the input layer of the network for Cukurova region. Precipitable water was the output. Correlation coefficient (R-2) between the predicted and measured values for monthly mean daily sum with LM method values was found to be 94.00% (training), 91.84% (testing), respectively. The findings revealed that the ANN-based prediction technique for estimating PW values is as effective as meteorological radiosonde observations. In addition, the results suggest that ANN method values be used so as to predict the precipitable water.
dc.identifier.doi10.1007/s10661-011-1953-6
dc.identifier.endpage147
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue1
dc.identifier.pmid21374043
dc.identifier.scopus2-s2.0-82255163990
dc.identifier.scopusqualityQ2
dc.identifier.startpage141
dc.identifier.urihttps://doi.org/10.1007/s10661-011-1953-6
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6043
dc.identifier.volume184
dc.identifier.wosWOS:000297520600012
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironmental Monitoring and Assessment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectPrecipitable water
dc.subjectArtificial neural network
dc.subjectMeteorology
dc.subjectCukurova region
dc.titlePrecipitable water modelling using artificial neural network in Cukurova region
dc.typeArticle

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