Downscaling Modeling Using Support Vector Regression for Rainfall Prediction

Sanusi Sanusi (Bogor Agricultural University)
Agus Buono (Bogor Agricultural University)
Imas S Sitanggang (Bogor Agricultural University)
Akhmad Faqih (Bogor Agricultural University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 12, No 8: August 2014

Statistical downscaling is an effort to link global scale to local scale variable. It uses GCM model which usually used as a prime instrument in learning system of various climate. The purpose of this study is as a SD model by using SVR in order to predict the rainfall in dry season; a case study at Indramayu. Through the model of SD, SVR is created with linear kernel and RBF kernel. The results showed that the GCM models can be used to predict rainfall in the dry season. The best SVR model is obtained at Cikedung rain station in a linear kernel function with correlation 0.744 and RMSE 23.937, while the minimum prediction result is gained at Cidempet rain station with correlation 0.401 and RMSE 36.964. This accuracy is still not high, the selection of parameter values for each kernel function need to be done with other optimization techniques.