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FORECASTING MONTHLY RAINFALL IN PANGKEP REGENCY USING STATISTICAL DOWNSCALING MODEL WITH ROBUST PRINCIPAL COMPONENT REGRESSION TECHNIQUE

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 19 No. 2 (2025) · pp. 777–790

DOI
10.30598/barekengvol19iss2pp777-790
ISSN
2615-3017, 1978-7227
Published
2025-04-01
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

A General Circulation Model (GCM) is a global climate model commonly used to predict local-scale climate patterns. However, the spatial resolution of GCMs is typically on a global scale, which is inadequate for predicting local climate. Statistical downscaling (SD) is used to transform climate information from a global scale to a smaller scale for local-scale climate predictions. GCM data have large dimensions and high correlations between grids, so principal component regression (PCR) is used in SD. The minimum covariance determinant (MCD) and minimum vector variance (MVV) methods are used in principal component analysis to obtain robust principal components (PCs). The data used in this study were the monthly rainfall data in Pangkep Regency for the period from January 1999 to December 2022 as the response variable, which were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) Region IV Makassar. The predictor variable data were GCM precipitation data (64 variables) for the same period and three dummy variables. This study aimed to obtain rainfall forecasts in Pangkep Regency for the year 2023 based on a robust PCR model using results from MCD and MVV. The modeling results indicated that both the MCD and MVV methods provided similar model accuracy, with a coefficient of determination of approximately 91%. The PCR model with two PCs from the MVV method and dummy variables was identified as the best model for explaining the variability in rainfall data in Pangkep Regency. Additionally, the 2023 rainfall forecast results showed that both methods yielded relatively similar accuracy. The addition of dummy variables in the PCR model improved both the model accuracy and rainfall forecasts. The PCR model with three PCs from MVV and dummy principal component variables produced accurate rainfall forecasts based on a high correlation value (0.974) and the smallest mean absolute percentage error (7.290).

Collaboration network of the authors

24 authors · 58 co-authorship links

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  • Hasanuddin University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
M. Zaky Hisyam GozhiHasanuddin University3
Muh. Ikbal TaufikHasanuddin University2
Anisa KalondengHasanuddin University2
Erna Tri HerdianiHasanuddin University2
Rizki AdiputraHasanuddin University1
Risma RismaAffiliation unknown1
Dewi Santika Upa P.Affiliation unknown1
Raupong RaupongHasanuddin University1
Fitri AnnisaAffiliation unknown1
Andi Kresna JayaHasanuddin University1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Georgina Maria TinungkiHasanuddin University1
Hendriete Tiur Marowi WaibusiHasanuddin University1
Andi Sri YuliantiHasanuddin University1
Mayashari MayashariHasanuddin University1
Eunike Laurine RandaHasanuddin University1
Sitti Aisyah SuriandaHasanuddin University1
Guntur Dwi PutraHasanuddin University1
Rahmi Nurul Ainun FitrahHasanuddin University1
Siswanto SiswantoHasanuddin University1
Syamsuddin ToahaHasanuddin University1
Aidul Fitri MustaminHasanuddin University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">FORECASTING MONTHLY RAINFALL IN PANGKEP REGENCY USING STATISTICAL DOWNSCALING MODEL WITH ROBUST PRINCIPAL COMPONENT REGRESSION TECHNIQUE</dc:title>
	<dc:creator>Sahriman, Sitti</dc:creator>
	<dc:creator>Anisa, Anisa</dc:creator>
	<dc:description xml:lang="en-US">A General Circulation Model (GCM) is a global climate model commonly used to predict local-scale climate patterns. However, the spatial resolution of GCMs is typically on a global scale, which is inadequate for predicting local climate. Statistical downscaling (SD) is used to transform climate information from a global scale to a smaller scale for local-scale climate predictions. GCM data have large dimensions and high correlations between grids, so principal component regression (PCR) is used in SD. The minimum covariance determinant (MCD) and minimum vector variance (MVV) methods are used in principal component analysis to obtain robust principal components (PCs). The data used in this study were the monthly rainfall data in Pangkep Regency for the period from January 1999 to December 2022 as the response variable, which were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) Region IV Makassar. The predictor variable data were GCM precipitation data (64 variables) for the same period and three dummy variables. This study aimed to obtain rainfall forecasts in Pangkep Regency for the year 2023 based on a robust PCR model using results from MCD and MVV. The modeling results indicated that both the MCD and MVV methods provided similar model accuracy, with a coefficient of determination of approximately 91%. The PCR model with two PCs from the MVV method and dummy variables was identified as the best model for explaining the variability in rainfall data in Pangkep Regency. Additionally, the 2023 rainfall forecast results showed that both methods yielded relatively similar accuracy. The addition of dummy variables in the PCR model improved both the model accuracy and rainfall forecasts. The PCR model with three PCs from MVV and dummy principal component variables produced accurate rainfall forecasts based on a high correlation value (0.974) and the smallest mean absolute percentage error (7.290).</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2025-04-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/14257</dc:identifier>
	<dc:identifier>10.30598/barekengvol19iss2pp777-790</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application; 777-790</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol19iss2year2025</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/14257/9954</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Sitti Sahriman, Anisa Anisa</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>