MODELLING INDIAN OCEAN AIR TEMPERATURE USING ADDITIVE MODEL

Abstract

In this study, we used the fluctuating air temperature dataset. The change is caused by data fluctuations, trend, seasonality, cyclicity and irregularities. The generalized additive model (GAM) data approach is used to describe these phenomena. The aim of this research is to find out the factors that affect the air temperature in the Indian Ocean, find a suitable model, and obtain the best model from three approximate methods, namely the Linear Model (LM), the Generalized Linear Model (GLM), and the GAM models, which use a dataset of factors that affect the temperature of the Indian Ocean (close to Aceh region). For the air temperature of α = 0.05, the significant effects are precipitation, relative humidity, sea surface temperature, and the wind speed. The LM, GLM and GAM models are quite feasible because they all meet and pass the classical hypothesis tests, namely the normality test, multicollinearity test, the heteroscedasticity test, and the autocorrelation test. The appropriate model is GAM model based on adaptive smoothers. Compared to the LM, GLM and GAM models, GAM model with the adaptive smoothers base gave smallest AIC values of 4552.890 and 2392.396 where modeling was without and with time variable respectively. Therefore, it can be said that the correct model used at air temperature is the GAM model for adaptive smoothers base.