Pemodelan Clusterwise Regression Pada Statistical Downscaling Untuk Pendugaan Curah Hujan Bulanan

Abstract

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.

Penerapan Machine Learning Clustering K-Means dan Linear Regression Dalam Penentuan Tingkat Resiko Tuberkulosis Paru

“…Ada tiga metode untuk clusterwise regression yaitu regresi linier atau linear regression, metode campuran terbatas atau finite mixture method (FMM) dan metode pembobotan cluster atau cluster-weighted method (CWM) [19].…”