PEMODELAN CLUSTERWISE REGRESSION PADA STATISTICAL DOWNSCALING UNTUK PENDUGAAN CURAH HUJAN BULANAN

Victor Pandapotan Butar-butar (Department of Statistics, IPB University, Indonesia)
Agus M Soleh (Department of Statistics, IPB University, Indonesia)
Aji H Wigena (Department of Statistics, IPB University, Indonesia)

Indonesian Journal of Statistics and Its Applications
Vol 3 No 3 (2019)

Statistical downscaling (SDS) is one of the developing models for rainfall estimation. The SDS model is a regression model used to analyze the relation of global (GCM output) and local data (rainfall). Rainfall has large variance so that clustering is needed to minimize the variance. One of the analytical methods that can be used in clustering rainfall estimation is cluster wise regression. There are three Methods for Clusterwise regression namely Linear Regresion, Finite Mixture Method (FMM) and Cluster-Weighted Method (CWM). This study used GCM outputs data namely CFRSv2 as a covariate. The response variable is rainfall data in four stations such as Bandung, Bogor, Citeko and Jatiwangi from BMKG. The purpose of this study is to increase the accuracy of rainfall estimation using the three methods and compare the clusterwise regression with PCR and PLS models. Based on the value of RMSEP, the clusterwise regression with FMM was the best method to estimate rainfall in four stations.