Rainfall Forecasting Using the Singular Spectrum Analysis (SSA) Method
DOI:
https://doi.org/10.30812/varian.v8i2.4571Keywords:
Forcasting, Rainfall, Singular Spectrum AnalysisAbstract
This study aims to evaluate the accuracy and performance of rainfall data forecasting in the city of Parepare using the Singular Spectrum Analysis (SSA) method. Situated in South Sulawesi Province, Parepare City is characterized by high rainfall intensity, which increases the likelihood of natural hazards such as flooding and landslides. These disasters have the potential to negatively impact key sectors, including economic activity, tourism, and transportation. Therefore, reliable rainfall prediction plays a crucial role in establishing a robust disaster early warning system. Monthly rainfall measurements from two stations, Bukit Harapan and Bulu Dua, are analyzed. The results reveal a Root Mean Square Error (RMSE) of 191.0566 for Bukit Harapan station and 346.023 for Bulu Dua station, underscoring the method's forecasting accuracy. A 12-month forecast predicts consistently high monthly rainfall in Parepare City, with the highest rainfall expected in December 2024 at Bukit Harapan station and in January 2024 at Bulu Dua station. Conversely, the lowest rainfall at both stations is anticipated in July 2024. Forecasts predicting increased rainfall during certain periods, especially in December and January, provide critical insights for strengthening disaster preparedness and informing mitigation strategies. This information also plays a key role in minimizing adverse effects on the economic, transportation, and tourism sectors, while promoting more efficient and sustainable management of water resources.
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