Rainfall Prediction in the Krueng Pase Watershed Using Support Vector Regression
Corresponding author: Teuku Ferijal
Abstract
In the present work, the Support Vector Regression (SVR) model was employed to predict monthly rainfall amounts in the Krueng Pase Watershed, Indonesia, for the period 1992-2020. The meteorological factors considered were: the number of rainy days, temperature, humidity, solar exposure, and wind velocity. Three SVR kernel functions were applied (linear, Radial Basis Function (RBF), and polynomial). A comparison was conducted using an 80/20 training-testing split together with a grid search for optimization. The model performance was evaluated by means of the Root Mean Square Error (RMSE) and the coefficient of determination (R2). The results indicated that the polynomial kernel exhibited the best performance with an RMSE of 57.71 mm and R2 = 025. The Kendall-Tau analysis revealed that the number of rainy days and humidity were the most significant positive predictors, whereas temperature and solar exposure had negative impacts. The moderate predictive skill indicated that large-scale climatic drivers were missing, yet it confirmed that SVR provides a strong, data-driven method for regional rainfall forecasting.
Keywords:
rainfall prediction, Support Vector Regression (SVR), Krueng Pase, Machine Learning (ML)Downloads
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