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Pemodelan Statistical Downscaling dengan Regresi Modifikasi Jackknife Ridge Dummy Berbasis K-means untuk Pendugaan Curah Hujan

Dewi Santika Upa P. Bio | Sitti Sahriman
DOI: https://doi.org/10.20956/ejsa.v2i1.11189
Article History

Submited : September 1, 2020
Published : May 15, 2021

Indonesia is a tropical country, which only has two seasons throughout the year, namely the dry season and the rainy season. Thus, it is likely that rain will continue to fall during the dry season, which has a serious impact on various sectors of life. General Circulation Model (GCM) is used to deal with climate change, but the GCM cannot conduct simulations well for local scale climate variables. Therefore, Statistical Downscaling (SD) is used to predict local scale rainfall in the district of Pangkep based on square GCM (CMIP5) 8 × 8 grid data. Modified jackknife ridge regression is used to overcome multicollinearity problems that occur in GCM-lag data. Three dummy variables were added as predictor variables for the model to overcome the heterogeneity of the various forms. SD model MJR dummy regression gives good results based on the coefficient of determination and high correlation with lower root mean square error and root mean square error prediction.

References

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