Comparison of Support Vector Regression and Random Forest Methods for Rainfall Prediction in Makassar City - Ilmadinah Kadir; Wahidah Sanusi; Ja'faruddin Ja'faruddin (Journal of Mathematics, Computations and Statistics, 2026)
This study aims to compare the performance of Support Vector Regression (SVR) and Random Forest (RF) methods in predicting daily rainfall in Makassar City and to identify the most influential meteorological factors. The dataset consists of daily climate data from 2019 to 2024, including rainfall as the response variable and temperature, humidity, wind speed, and sunshine duration as predictor variables. Data preprocessing was conducted through missing value imputation, time-series structuring, and normalization using the Z-score method for the SVR model. The SVR model was developed using several kernel functions, including linear, polynomial, radial basis function (RBF), and sigmoid, with hyperparameter tuning performed using grid search and k-fold cross-validation. Meanwhile, the Random Forest model was constructed using bootstrap aggregation and random feature selection, with optimal parameters determined based on the minimum out-of-bag (OOB) error. The results show that the SVR model with the RBF kernel achieved the best performance, with RMSE of 16.52 mm and MAE of 9.01 mm, outperforming the Random Forest model, which produced RMSE of 18.15 mm and MAE of 10.93 mm. Furthermore, feature importance analysis indicates that humidity and temperature are the most dominant variables influencing rainfall. Therefore, the SVR method is more accurate and reliable for rainfall prediction in Makassar City.