Quantile spline regression on statistical downscaling model to predict extreme rainfall in Indramayu

Abstract

Statiscal Downscaling (SD) was a method to model the relationship between local scale data as response variable and global scale data as predictor variable. The response variable was the rainfall and the predictor variable was the global circulation model (GCM) ouput. In general, GCM output had a large dimension and multicollinearity so the principal component analysis was used to solve this problem. This study modeled the rainfall with the selected principal component using quantile regression. This functional relationship could be parametric or nonparametric relationship. In this case, nonparametric functional relationship used spline to accommodate the extreme value with the quantile regression. The result showed that the quantile spline model was better than the quantile polynomial regression model especially in predicting the extreme values in the 90 th and 95 th quantile with correlation values of 0.95 and 0.93.