The application of seasonal autoregressive integrated moving average (SARIMA) model in forecasting Malaysia mean sea level.

The rising mean sea level is a significant concern globally, particularly in coastal regions such as Malaysia. Accurate prediction and forecasting of sea level variations are crucial for effective coastal management and adaptation strategies. This study aims to investigate the predictive capabilities of the Seasonal Autoregressive Integrated Moving-Average (SARIMA) model for Malaysia mean sea level. The research utilizes historical sea level data obtained from Permanent Service for Mean Sea Level (PSMSL) at three selected stations in Malaysia, namely Pulau Pinang, Pelabuhan Kelang, and Lumut. The SARIMA model is implemented to capture the seasonal patterns, autoregressive behavior, and short-term dependencies in the sea level time series. The predictive performance of the SARIMA model is assessed using appropriate evaluation metrics, including root mean squared error (RMSE) and mean absolute error (MAE). Additionally, the forecasted sea level values are compared with observed data to validate the models accuracy. The findings of this study contribute to a better understanding of the application of Seasonal Autoregressive Integrated Moving Average (ARIMA) in forecasting Malaysia mean sea level dynamics and providing valuable insights for coastal hazard management. The research outcomes emphasize the potential of seasonal ARIMA modeling as a reliable tool for predicting and forecasting mean sea level in Malaysia. This aids in decision-making processes and supports climate change mitigation efforts. [ABSTRACT FROM AUTHOR]