Prediction of seawater salinity based on comparison of truncated spline estimators, Fourier Series and Kernel

Abstract

Salinity is one of the factors that affect salt production. Salinity is defined as the level of saltiness or too much salt in water. The salt in question is a variety of ions dissolved in water, including table salt (NaCl). The higher the level of NaCl contained, the better the quality of the salt formed. This low quality causes Indonesia to import salt, both consumption salt and industrial salt. Because most of the quality of salt still does not meet the criteria of SNI. For this reason, it is necessary to predict the salinity of seawater to help determine the next steps or policies in improving the quality of salt in Indonesia, especially in the Madura area. This research is examined in the form of a nonparametric regression curve estimator with a truncated spline estimator approach, Fourier series and kernel. From the comparison results, the best model for predicting seawater salinity is the estimator of the Fourier series base sine cosine with an oscillation parameter (k) of 2 with a GCV value of 5.017987 and MSE and a coefficient of determination of 0.06299933 and 94.64373%. So the prediction results obtained in this study are close to accurate with MAPE values of 0.07225208%, MSE of 0.0001441417 and coefficient of determination of 99.99%.