Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

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dc.authoridKALE, Semih/0000-0001-5705-6935
dc.contributor.authorKale, Semih
dc.date.accessioned2025-01-27T20:35:01Z
dc.date.available2025-01-27T20:35:01Z
dc.date.issued2020
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractAn accurate estimation of the sea surface temperature of great importance. Therefore, the objective of this work was to develop an adaptive neuro-fuzzy inference system (ANFIS) model to predict SST in the Canakkale Strait. The observed monthly air temperature, evaporation and precipitation data from the Canakkale meteorological observation station were used as input data. The Takagi-Sugeno fuzzy inference system was applied. The grid partition method (ANFIS-GP) and the subtractive clustering partitioning method (ANFIS-SC) were used with Gaussian membership functions to generate the fuzzy inference system. Six performance evaluation criteria were used to evaluate the developed SST prediction models, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and correlation of determination (R-2). The dataset was randomly divided into training and testing datasets for the machine learning process. Training data accounted for 75% of the dataset, while 25% of the dataset was allocated for testing in ANFIS. The hybrid algorithm was selected as a training algorithm the ANFIS. Simulation results revealed that the ANFIS-SC4 model provided a higher correlation coefficient of 0.96 between the observed and predicted SST values. The results of this study suggest that the developed ANFIS model can be applied for predicting sea surface temperature around the world.
dc.identifier.doi10.1515/ohs-2020-0031
dc.identifier.endpage373
dc.identifier.issn1730-413X
dc.identifier.issn1897-3191
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85097226802
dc.identifier.scopusqualityQ3
dc.identifier.startpage354
dc.identifier.urihttps://doi.org/10.1515/ohs-2020-0031
dc.identifier.urihttps://hdl.handle.net/20.500.12428/23545
dc.identifier.volume49
dc.identifier.wosWOS:000594531400003
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWalter De Gruyter Gmbh
dc.relation.ispartofOceanological and Hydrobiological Studies
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectartificial intelligence
dc.subjectANFIS
dc.subjectfuzzy
dc.subjectforecast
dc.subjectmodelling
dc.subjectwater temperature
dc.subjectSST
dc.subjectclimate
dc.titleDevelopment of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)
dc.typeArticle

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