| dc.contributor.author | Adhani'., Gita | |
| dc.contributor.author | Buono, Agus | |
| dc.contributor.author | Faqih, Ahmad | |
| dc.date.accessioned | 2016-05-21T04:38:54Z | |
| dc.date.available | 2016-05-21T04:38:54Z | |
| dc.date.issued | 2013-09 | |
| dc.identifier.isbn | 978-979-1421-19-5 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/80645 | |
| dc.description.abstract | Various c1imate disasters in lndonesia
are mostly related to the El Nino Southern
Oscillation (ENSO) phenomenon. The variability of
etimate especially rainfall is strongly related to this
phenomenon. Southern Oscillation Index (SOl) and
sea surface temperature anomaly (SST A) at
Nin03.4 region are two common indicators used to
monitor phenomenon of El Nino and La Nina.
Furthermore, SOl and NINO SSTA can be the
indicator to find the rainfall probability in a
particular season, related to the existing condition
of c1imate irregularities. This research was
conducted to estimate the rainfall during dry
season at Indramayu district. The basic method
used in this study was Support Vector Regression
(SVR). Predictors used were SOl and NIN03A sea
surface temperature (SST) data. The expcrtments
wcre conducted by comparing the model
performance and prediction rcsults, The training
set was c1ustered in advance and then SVR model
was generated using RBF kernel based on their
c1ustering r esult. This research obtaincd an SVR
model with correlation coefficient of 0.76 and
NRMSE error value of 1.73. | id |
| dc.language.iso | en | id |
| dc.publisher | other | id |
| dc.publisher | other | id |
| dc.title | Support Vector Regression Modelling for Rainfall Prediction in Dry Season Based on Southern Oscillation Index and NIN03.4 | id |
| dc.type | Article | id |
| dc.subject.keyword | Support Vector Regression | id |
| dc.subject.keyword | Southern Oscillation Index | id |
| dc.subject.keyword | Nino3.4 | id |
