Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

Abstract

Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.

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“…We use recent studies about water level classification, rainfall prediction, and flood prediction as references in this research. In the rainfall prediction using the SVM algorithm, the study shows that the best model produces a Root Mean Square Error (RMSE) value of 88.426 [6]. In flood prediction in Bangladesh using the K-NN algorithm, the study shows that the K-NN algorithm yields an average accuracy value of 94.91%, an average precision value of 92%, and an average recall value of 91% [7].…”

“…In solving classification problems using non-linearly separable datasets, this algorithm uses a kernel function (kernel trick) for mapping the data into a high-dimensional feature space to obtain a hyperplane that separates the data into two classes [30]. Some of the more popular kernel functions often used in SVM are the polynomial, RBF, and sigmoid functions; these kernel functions use equations ( 4) to (6) to generate a hyperplane in the classification process [31].…”