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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) ESTIMASI: Journal of Statistics and Its Application Indonesian Journal of Statistics and Its Applications
Sahriman, Sitti
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Public Health Transportation Other

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Journal : Indonesian Journal of Statistics and Its Applications

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Statistical Downscaling Model with Jackknife Ridge Regression and Modified Jackknife Ridge Regression to Forecast Rainfall Sahriman, Sitti; Upa, Dewi
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p155-165

Abstract

Statistical downscaling (SD) is a transfer function that connects local scale rainfall data with global scale rainfall. Global-scale rainfall can be obtained from the Global Circulation Model (GCM) output. GCM simulates climate variables in the form of large-scale grids, causing a high correlation between the grids (multicollinearity). The methods used in SD modeling to overcome multicollinearity are Jackknife Ridge Regression (JRR) and Modified Jackknife Ridge Regression (MJR). The method is the development of the Ridge Regression (RR) method. This study aims to predict local rainfall data in Pangkep Regency (response variables) based on local scale GCM output rainfall data (predictor variables) with the JRR and MJR approaches. In addition, K-means cluster technique is used in determining dummy variables to overcome the heterogeneity of the various remaining models. Results using training data (1990-2017 period) show that the MJR method is better at explaining the diversity of data based on a higher R2 value (68%) and a lower Root Mean Square Error / RMSE value (165.57) than the JRR method (R2 amount is 67 and RMSE amount is 167.72). Model validation using data testing (2018 period) also shows the same results, namely MJR is better than JRR. Other than that, the addition of dummy variables can improve the accuracy of the model in estimating rainfall data. Adding a dummy variable to the model results in a high R2 (range between 94% -95%) with a lower RMSE value (range between 66.60-67.69).
Co-Authors Aidawayati, Aidawayati Amir Kamal Amir Andi Kresna Jaya Andrianingrum, Amalia Anisa Anisa Anna Islamiyati Anwar, Andi M. B., Hasmawati Edy Saputra Rusdi Georgina Maria Tinungki Hisyam, M. Zaky Gozhi Jaya, A. Kresna Jusmawati Jusmawati, Jusmawati Jusmawati Massalesse Kasbawati Kasbawati Naimah Aris Nirwan Nirwan Nur Erawaty Nurhidayatullah Nurhidayatullah, Nurhidayatullah Putra, Guntur Dwi Ramadhan, Muh. Nur Bahri Randa, Eunike Laurine Rangkuti, Aidawayati Raupong, Raupong Sirajang, Nasrah Sri Astuti Thamrin Surianda, Sitti Aisyah Taufik, Muh. Ikbal Thoha, Syamsuddin Upa, Dewi Wahda, Wahda Wahyuni, Alfiana Waibusi, Hendriete Tiur Marowi Yulianti, Andi Sri Yusuf, Anisa Haura Salsa Fatih