Prediction of Sea Level by Using Autoregressive Integrated Moving Average (ARIMA): Case Study in Tanjung Intan Harbour Cilacap, Indonesia

Abstract

Sea Level forecasting is vital for shores engineering applications such as for engineering construction plan in the shore or in offshore, and routing of ships at harbor. Researchers have been conducting many methods to predict sea levels, such as Artificial Neural Network, SARIMA, and ARIMA. In this paper, we will use a model of Autoregressive Integrated Moving Average (ARIMA) to predict sea level in Cilacap, Indonesia. The ARIMA parameters are obtained by conducting parameter tuning so that the model gives the lowest root mean square error value (RMSE) and the highest correlation coefficient.