Virtual Booth

Understanding rainfall trends is vital for managing water resources, agriculture, and flood risks in Malaysia. This study applies time series forecasting to analyze historical rainfall data and identify significant seasonal and long-term patterns. By using the Seasonal and Trend decomposition using Loess (STL) combined with ARIMA modeling, the forecast provides more accurate and interpretable predictions. The results offer insights into future rainfall behavior, which can support early warning systems and policy planning. This approach demonstrates the value of data-driven forecasting in addressing climate-related challenges and helping communities prepare for extreme weather events in a changing environment.