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Accurate prediction of rainfall is crucial for anticipating and preparing for potential disasters such as floods or droughts in specific areas. This study introduces the Autocorrelation Function (ACF) method as a potent tool for modeling these dependencies within Pahang rainfall data. By examining the temporal relationships between current and past values, the ACF method yields valuable insights. This study leverages ACF to forecast rainfall at specific time intervals, capturing crucial temporal patterns within the daily, weekly maximum and monthly maximum interval. Our findings highlight the remarkable accuracy of the ACF method in enhancing Support Vector Machine (SVM) model performance, especially when there is a strong relationship between the current and past values of the rainfall series. Moreover, the forecasting analysis reveals that incorporating the ACF method into the SVM model surpasses conventional techniques, as evidenced by the lowest root mean square error (RMSE) values. In conclusion, this study advances our comprehension of rainfall dynamics in Pahang, strengthening the capabilities for improved forecasting and risk mitigation strategies. [ABSTRACT FROM AUTHOR]

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]