Proceedings of KNASTIK
PEMODELAN DOWNSCALING LUARAN GCM DAN ANOMALI SST NINO 3.4 MENGGUNAKAN SUPPORT VECTOR REGRESSION (Studi Kasus Curah Hujan Bulanan Indramayu)
Abstract
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 result 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.
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 result 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.
Keywords
Downscaling, ENSO, Luaran GCM, SST Nino 3.4 and SVR
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