STATISTICAL MODELING FOR DOWNSCALING USING PRINCIPAL COMPONENT REGRESSION AND DUMMY VARIABLES: A CASE OF SIAK DISTRICT
Abstract
Indonesia, as a tropical country, is characterized by two primary seasons: the rainy season and the dry season. It is evident that meteorological shifts can exert considerable influence on the agricultural sector, a notable example being the cultivation of palm oil. Consequently, the ability to predict rainfall has emerged as a pivotal element in the broader endeavor to mitigate the adverse effects of climate change. This study employs statistical downscaling using the Principal Component Regression (PCR) approach to model rainfall predictions. The issue of multicollinearity, a common occurrence in Global Circulation Model (GCM) data, is addressed through the use of Principal Component Regression (PCR). This method has been demonstrated to stabilize the model structure and reduce variance in the regression coefficients. The data utilized encompass observed rainfall from LIBO Estate, which is owned by PT SMART Tbk (SMART Research Institute), for the period from 2013 to 2022. This data serves as the response variable, while the CMIP6 GCM simulation output data functions as the predictor variable. The findings indicated that the initial PCR model exhibited an RMSE value ranging from 97.06 to 131.69, along with an R² value ranging from 14.25% to 20.49%. The incorporation of dummy variables into the model resulted in a substantial enhancement in its performance, as evidenced by a decline in RMSE to 24.46–35.83 and an increase in R² to 89.02%–90.24%. The findings indicate that the use of PCR with dummy variables is an effective approach for enhancing the accuracy of rainfall modeling through statistical downscaling.
Downloads
References
N. Dharmawati, S. Suntoro, K. Komariah, and H. Hermantoro, “POTENTIAL RAINWATER AVAILABILITY AND CROP WATER REQUIREMENT OF OIL PALM CROPS DUE TO CLIMATE CHANGE,” IOP Conf Ser Earth Environ Sci, vol. 1482, p., 2025, doi: https://doi.org/10.1088/1755-1315/1482/1/012006.
J. P. Rajakal, V. Andiappan, and Y. K. Wan, “MATHEMATICAL APPROACH TO FORECAST OIL PALM PLANTATION YIELD UNDER CLIMATE CHANGE UNCERTAINTIES,” Chem Eng Trans, vol. 83, pp. 115–120, 2021, doi: https://doi.org/10.3303/CET2183020.
A. Abubakar, M. Ishak, and A. Makmom, “NEXUS BETWEEN CLIMATE CHANGE AND OIL PALM PRODUCTION IN MALAYSIA: A REVIEW,” Environ Monit Assess, vol. 194, p., 2022, doi: https://doi.org/10.1007/s10661-022-09915-8.
I. Pradiko, H. Hariyadi, and T. June, “QUANTIFICATION OF CLIMATE FACTORS CONTRIBUTING TO VARIATION OF OIL PALM YIELD,” Jurnal Penelitian Kelapa Sawit, p., 2023, doi: https://doi.org/10.22302/iopri.jur.jpks.v31i2.222.
Y. Go, Y.-L. Tan, and T.-H. Yiew, “SENSITIVITY OF OIL PALM YIELD IN INDONESIA TO CLIMATE CHANGE: EVIDENCE FROM THRESHOLD COINTEGRATION MODELS,” Environ Dev Sustain, p., 2024, doi: https://doi.org/10.1007/s10668-024-05635-w.
S. Oktarina, R. Nurkhoiry, and I. Pradiko, “THE EFFECT OF CLIMATE CHANGE TO PALM OIL PRICE DYNAMICS: A SUPPLY AND DEMAND MODEL,” IOP Conf Ser Earth Environ Sci, vol. 782, p., 2021, doi: https://doi.org/10.1088/1755-1315/782/3/032062.
S. Sahriman, A. Djuraidah, and A. H. Wigena, “APPLICATION OF PRINCIPAL COMPONENT REGRESSION WITH DUMMY VARIABLE IN STATISTICAL DOWNSCALING TO FORECAST RAINFALL,” Open J Stat, vol. 04, pp. 678–686, 2014, doi: http://dx.doi.org/10.4236/ojs.2014.49063.
L. Safitri, H. Hermantoro, S. Purboseno, V. Kautsar, S. K. Saptomo, and A. Kurniawan, “WATER FOOTPRINT AND CROP WATER USAGE OF OIL PALM (ELEASIS GUINEENSIS) IN CENTRAL KALIMANTAN: ENVIRONMENTAL SUSTAINABILITY INDICATORS FOR DIFFERENT CROP AGE AND SOIL CONDITIONS,” Water (Switzerland), vol. 11, no. 35, pp. 1–16, 2018, doi: https://doi.org/10.3390/w11010035.
N. S. Samsuddin, N. F. A. Aziz, B. Balachandran, and J. Ali, “ECONOMIC CLIMATE MODEL ON THE PALM PRODUCTION: EMPIRICAL EVIDENCE FOR MALAYSIA AND INDONESIA,” Malaysian Journal of Consumer and Family Economics, p., 2024, doi: https://doi.org/10.60016/majcafe.v33.17.
A. Thant and W. Aye, “FUTURE PREDICTIONS OF RAINFALL USING GCMS: A CASE STUDY FOR MANDALAY, MYANMAR,” International Journal of Scientific and Research Publications (IJSRP), p., 2019, doi: https://doi.org/10.29322/ijsrp.9.09.2019.p9314.
B. Deepthi and B. Sivakumar, “SHORTEST PATH LENGTH FOR EVALUATING GENERAL CIRCULATION MODELS FOR RAINFALL SIMULATION,” Clim Dyn, vol. 61, pp. 3009–3028, 2023, doi: https://doi.org/10.1007/s00382-023-06713-x.
E. Rocheta, M. Sugiyanto, F. Johnson, J. Evans, and A. Sharma, “HOW WELL DO GENERAL CIRCULATION MODELS REPRESENT LOW‐FREQUENCY RAINFALL VARIABILITY?,” Water Resour Res, vol. 50, pp. 2108–2123, 2014, doi: https://doi.org/10.1002/2012WR013085.
X. Su, W. Shao, J. Liu, and Y. Jiang, “MULTI-SITE STATISTICAL DOWNSCALING METHOD USING GCM-BASED MONTHLY DATA FOR DAILY PRECIPITATION GENERATION,” Water (Basel), vol. 12, pp. 1–21, 2020, doi: https://doi.org/10.3390/w12030904.
M. D. Saputra, A. F. Hadi, A. Riski, and D. Anggraeni, “PRINCIPAL COMPONENT REGRESSION IN STATISTICAL DOWNSCALING WITH MISSING VALUE FOR DAILY RAINFALL FORECASTING,” International Journal of Quantitative Research and Modeling, vol. 2, no. 3, pp. 139–146, 2021, doi: https://doi.org/10.46336/ijqrm.v2i3.151.
A. Mulyati, A. Wigena, and A. Djuraidah, “STATISTICAL DOWNSCALING USING KERNEL QUANTILE REGRESSION TO PREDICT EXTREME RAINFALL,” Int J Sci Basic Appl Res, vol. 42, pp. 1–9, 2018, [Online]. Available: https://consensus.app/papers/statistical-downscaling-using-kernel-quantile-djuraidah-mulyati/ed63469d34a65eb28c90670b1cbfaf24/
S. Sahriman and A. S. Yulianti, “STATISTICAL DOWNSCALING MODEL WITH PRINCIPAL COMPONENT REGRESSION AND LATENT ROOT REGRESSION TO FORECAST RAINFALL IN PANGKEP REGENCY,” Barekeng: Journal of Mathematics and Its Applications, vol. 17, no. 1, pp. 0401–0410, 2023, doi: https://doi.org/10.30598/barekengvol17iss1pp0401-0410.
S. Sahriman and A. Anisa, “FORECASTING MONTHLY RAINFALL IN PANGKEP REGENCY USING STATISTICAL DOWNSCALING MODEL WITH ROBUST PRINCIPAL COMPONENT REGRESSION TECHNIQUE,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, p., 2025, doi: https://doi.org/10.30598/barekengvol19iss2pp777-790.
S. Sahriman, E. L. Randa, S. A. Surianda, M. Z. G. Hisyam, Muh. I. Taufik, and G. D. Putra, “RAINFALL FORECASTING OF SALT PRODUCING AREAS IN PANGKEP REGENCY USING STATISTICAL DOWNSCALING MODEL WITH LINEARIZED RIDGE REGRESSION DUMMY,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 18, no. 1, pp. 0483–0492, Mar. 2024, doi: https://doi.org/10.30598/barekengvol18iss1pp0483-0492.
P. Loganathan and A. B. Mahindrakar, “STATISTICAL DOWNSCALING USING PRINCIPAL COMPONENT REGRESSION FOR CLIMATE CHANGE IMPACT ASSESSMENT AT THE CAUVERY RIVER BASIN,” Journal of Water and Climate Change, vol. 12, no. 6, pp. 2314–2324, 2021, doi: https://doi.org/10.2166/wcc.2021.223.
Lawrence Livermore National Laboratory, “CMIP6.” [Online]. Available: https://aims2.llnl.gov/search/cmip6/
A. H. Wigena, “PEMODELAN STATISTICAL DOWNSCALING DENGAN REGRESI PROJECTION PURSUIT UNTUK PERAMALAN CURAH HUJAN BULANAN,” Institute Pertanian Bogor, 2006.
W. Surianto, “PEMODELAN STATISTICAL DOWNSCALING MENGGUNAKAN COMBINE CLUSTERWISE REGRESSION UNTUK PENDUGAAN CURAH HUJAN HARIAN,” Institute Pertanian Bogor, 2022.
A. Gwelo, “PRINCIPAL COMPONENTS TO OVERCOME MULTICOLLINEARITY PROBLEM,” Oradea Journal of Business and Economics, p., 2019, doi: https://doi.org/10.47535/1991OJBE062.
M. Raheem, N. Udoh, and A. T. Gbolahan, “CHOOSING APPROPRIATE REGRESSION MODEL IN THE PRESENCE OF MULTICOLINEARITY,” Open J Stat, p., 2019, doi: https://doi.org/10.4236/OJS.2019.92012.
R. A. Johnson and D. W. Wichern, APPLIED MULTIVARIATE STATISTICAL ANALYSIS, 6 Ed. United States of America: Pearson Prentice Hall, 2007.
D. N. Gujarati and D. C. Porter, BASIC ECONOMETRICS, 5th ed. United States: The McGraw-Hill Companies, Inc., 2009.
D. A. Setiady and H. Leong, “IMPLEMENTATION OF K-MEANS ALGORITHM ELBOW METHOD AND SILHOUETTE COEFFICIENT FOR RAINFALL CLASSIFICATION,” Proxies : Jurnal Informatika, p., 2024, doi: https://doi.org/10.24167/proxies.v4i1.12433.
G. Erda, C. Gunawan, and Z. Erda, “GROUPING OF POVERTY IN INDONESIA USING K-MEANS WITH SILHOUETTE COEFFICIENT,” Parameter: Journal of Statistics, vol. 3, no. 1, pp. 1–6, 2023, doi: https://doi.org/10.29040/ijcis.v6i1.218.
M. Riasetiawan, A. Ashari, and P. Wahyu, “THE PERFORMANCE EVALUATION OF K-MEANS AND AGGLOMERATIVE HIERARCHICAL CLUSTERING FOR RAINFALL PATTERNS AND MODELLING,” 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 431–436, 2022, doi: https://doi.org/10.1109/ICITISEE57756.2022.10057729.
J. Feng and X. Wang, “IMPACT OF TWO TYPES OF LA NIÑA ON BOREAL AUTUMN RAINFALL AROUND SOUTHEAST ASIA AND AUSTRALIA,” Atmospheric and Oceanic Science Letters, vol. 11, pp. 1–6, 2018, doi: https://doi.org/10.1080/16742834.2018.1386538.
S. Zhong, Y. Zhang, and L. Jiang, “IMPACT OF DIFFERENT TYPES OF LA NIÑA DEVELOPMENT ON THE PRECIPITATION IN THE MARITIME CONTINENT,” Atmosphere-Ocean, vol. 62, pp. 254–267, 2024, doi: https://doi.org/10.1080/07055900.2024.2326611.
Copyright (c) 2026 Arisman Adnan, Elsa Riesta Alika, Divo Dharma Silalahi, Felia Rizki Aulia, Gustriza Erda
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
