The Modeling of Downscaling GCM output and SST Anomaly Nino 3.4 Using Support Vector Regression (A Case Study of The monthly Rainfall In Indramayu).

dc.contributor.advisor	Buono, Agus
dc.contributor.advisor	Mushthofa
dc.contributor.author	Maesya, Aries
dc.date.accessioned	2012-09-24T02:33:14Z
dc.date.available	2012-09-24T02:33:14Z
dc.date.issued	2012
dc.identifier.uri	http://repository.ipb.ac.id/handle/123456789/57492
dc.description.abstract	The objective of this research is to develop a downscaling model GCM output and SST anomaly Nino 3.4 as input in the training to predict a rainfall monthly in Indramayu. The techniques of a downscaling is used for a phenomenon indicators of El Nino and Southern Oscillation (ENSO) climate anomaly such as a Global Circulation Model (GCM) and Sea Surface Temperature (SST) nino 3.4 are commonly used as a primary study learn and understand the climate system. This research propose a method for developing a downscaling model GCM output and SST anomaly Nino 3.4 by using Support Vector Regression (SVR). The research showed that GCM output and SST anomaly Nino 3.4 can be approach the average value of monthly rainfall. The best result of prediction is Bondan station which has average correlation that is 0.700.	en
dc.subject	Downscaling	en
dc.subject	ENSO	en
dc.subject	GCM	en
dc.subject	SST Nino 3.4 and SVR	en
dc.title	The Modeling of Downscaling GCM output and SST Anomaly Nino 3.4 Using Support Vector Regression (A Case Study of The monthly Rainfall In Indramayu).	en