Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)
[ X ]
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Walter De Gruyter Gmbh
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
An 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.
Açıklama
Anahtar Kelimeler
artificial intelligence, ANFIS, fuzzy, forecast, modelling, water temperature, SST, climate
Kaynak
Oceanological and Hydrobiological Studies
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
49
Sayı
4
