Comparison of Salinity and Seawater Temperature Predictions Using Var and Biresponse Fourier Series Estimator

Abstract

Salinity is the concentration of dissolved salts in water. The salt in question is a variety of ions dissolved in water, including table salt (NaCl). Salinity and seawater temperature are one of the factors that affect salt production. The higher the NaCl content, the better the quality of the salt. Currently, people's salt production is still unable to meet the needs of national salt, especially industrial salt, because most of the quality of people's salt still does not meet the SNI criteria for industrial salt. Thus, it is necessary to predict the salinity and temperature of seawater to help determine the next steps or policies in improving the quality of people's salt. Predictions of salinity and seawater temperature were carried out by applying the Vector Autoregressive (VAR) Analysis method and nonparametric Fourier series regression with primary data of salinity and seawater temperature on the coast of Tlesah Tlanakan Beach, Pamekasan. The best model chosen is the model that has the smallest error size and the highest accuracy measure. The best models are nonparametric regression of the Fourier series of sine and cosine bases with the predicted result obtaining a MAPE value is 0.00496 and coefficient of determination is 100%.