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Yulianti, Andi, Islamiyati, Anna, Herdiani, Erna. (1403). The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data. , 35(2), 159-166. doi: 10.22059/jsciences.2024.375343.1007860
Andi Sri Yulianti; Anna Islamiyati; Erna Tri Herdiani. "The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data". , 35, 2, 1403, 159-166. doi: 10.22059/jsciences.2024.375343.1007860
Yulianti, Andi, Islamiyati, Anna, Herdiani, Erna. (1403). 'The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data', , 35(2), pp. 159-166. doi: 10.22059/jsciences.2024.375343.1007860
Yulianti, Andi, Islamiyati, Anna, Herdiani, Erna. The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data. , 1403; 35(2): 159-166. doi: 10.22059/jsciences.2024.375343.1007860

The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data

Journal of Sciences, Islamic Republic of Iran
دوره 35، شماره 2، تیر 2024، صفحه 159-166    اصل مقاله (271.8 K)
نوع مقاله: Original Paper
شناسه دیجیتال (DOI): 10.22059/jsciences.2024.375343.1007860
نویسندگان
Andi Sri Yulianti؛ Anna Islamiyati* ؛ Erna Tri Herdiani
Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, Indonesia
چکیده
Information regarding rainfall can be obtained from global data, namely the global climate model that can be accessed through the statistical downscaling approach. Linear spline quantile regression with principal component is a statistical method that can be employed in statistical downscaling to address multicollinearity and outliers in data by using nonparametric estimators. This method is applied to rainfall data in Pangkep Regency from January 2008 to December 2022 as the response variable and global climate model data as the predictor variable. The aim of this research is to obtain the best regression model used for predicting rainfall data. The results obtained indicate that statistical downscaling with two principal components at the 0.50 quantile with respective knot points of -10.20 and -0.30 is the best model with the lowest generalized cross-validation value. The forecasted rainfall data using this model shows a high level of accuracy with a correlation of 89%.
کلیدواژه‌ها
Principal Component؛ Quantile Regression؛ Rainfall؛ Spline؛ Statistical Downscaling
مراجع
  1. Chaudhuri RR, Sharma P. An integrated stochastic approach for extreme rainfall analysis in the National Capital Region of India. J Earth Syst Sci. 2021;130(16):1–15.
  2. Tunas IG, Azikin H, Oka GM. Impact of Extreme Rainfall on Flood Hydrographs. IOP Conf Ser Earth Environ Sci. 2021;884(1):1–6.
  3. Mohsenipour M, Shahid S, Ziarh GF, Yaseen ZM. Changes in monsoon rainfall distribution of Bangladesh using quantile regression model. Theor Appl Climatol. 2020;142:1329–42.
  4. Keller AA, Garner KL, Rao N, Knipping E, Thomas J. Downscaling approaches of climate change projections for watershed modeling: Review of theoretical and practical considerations. PLOS Water. 2022;1(9):1–20.
  5. Mulyati AE, Wigena AH, Djuraidah A. Statistical Downscaling Using Kernel Quantile Regression to Predict Extreme Rainfall. Int J Sci Basic Appl Res. 2018;42(2):1–9.
  6. Singh P, Shamseldin AY, Melville BW, Wotherspoon L. Development of statistical downscaling model based on Volterra series realization, principal components and ridge regression. Model Earth Syst Environ. 2023;9(3):3361–80.
  7. Kalu I, Ndehedehe CE, Ferreira VG, Janardhanan S, Currell M, Kennard MJ. Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model. Sci Rep. 2024;14(1):1–19.
  8. Sahriman S, Djuraidah A, Wigena AH. Application of Principal Component Regression with Dummy Variable in Statistical Downscaling to Forecast Rainfall. Open J Stat. 2014;04(09):678–86.
  9. Lu Z, Guo Y, Zhu J, Kang N. Seasonal forecast of early summer rainfall at stations in south china using a statistical downscaling model. Weather Forecast. 2020;35(4):1633–43.
  10. Pahlavan HA, Zahraie B, Nasseri M, Varnousfaderani AM. Improvement of multiple linear regression method for statistical downscaling of monthly precipitation. Int J Environ Sci Technol. 2018;15(9):1897–912.
  11. Salimi AH, Samakosh JM, Sharifi E, Hassanvand MR, Noori A, Rautenkranz H Von. Optimized artificial neural networks-based methods for statistical downscaling of gridded precipitation data. Water (Switzerland). 2019;11(1653):1–20.
  12. Sharifi E, Steinacker R, Saghafian B. Multi time-scale evaluation of high-resolution satellite-based precipitation products over northeast of Austria. Atmos Res. 2018;206:1–33.
  13. Uranchimeg S, Kwon HH, Kim B, Kim TW. Changes in extreme rainfall and its implications for design rainfall using a Bayesian quantile regression approach. Hydrol Res. 2020;51(4):699–719.
  14. Malik N, Bookhagen B, Mucha PJ. Spatiotemporal patterns and trends of Indian monsoonal rainfall extremes. Geophys Res Lett. 2016;43(4):1710–7.
  15. Wagesho N, Claire M. Analysis of Rainfall Intensity-Duration-Frequency Relationship for Rwanda. J Water Resour Prot. 2016;8(7):706–23.
  16. Shiau JT, Huang WH. Detecting distributional changes of annual rainfall indices in Taiwan using quantile regression. J Hydro-Environment Res. 2014;9(3):1–13.
  17. Wigena AH, Djuraidah A, Rizki A. Semiparametric modeling in statistical downscaling to predict rainfall. Appl Math Sci. 2015;9(88):4371–82.
  18. Sharifi E, Saghafian B, Steinacker R. Downscaling Satellite Precipitation Estimates With Multiple Linear Regression, Artificial Neural Networks, and Spline Interpolation Techniques. J Geophys Res Atmos. 2019;124(2):789–805.
  19. Islamiyati A. Spline Longitudinal Multi-response Model for the Detection of Lifestyle- Based Changes in Blood Glucose of Diabetic Patients. Curr Diabetes Rev. 2021;18(7):98–104.
  20. Sachindra DA, Ahmed K, Rashid MM, Sehgal V, Shahid S, Perera BJC. Pros and cons of using wavelets in conjunction with genetic programming and generalised linear models in statistical downscaling of precipitation. Theor Appl Climatol. 2019;138(1):617–38.
  21. Lestari B, Fatmawati, Budiantara IN, Chamidah N. Estimation of Regression Function in Multi-Response Nonparametric Regression Model Using Smoothing Spline and Kernel Estimators. J Phys Conf Ser. 2018;1097(1):1–9.
  22. Goldameir NE, Djuraidah A, Wigena AH. Quantile Spline Regression on Statistical Downscaling Model to Predict Extreme Rainfall in Indramayu. Appl Math Sci. 2015;9(126):6263–72.
  23. Islamiyati A, Kalondeng A, Sunusi N, Zakir M, Amir AK. Biresponse nonparametric regression model in principal component analysis with truncated spline estimator. J King Saud Univ - Sci. 2022;34(3):1–9.
  24. Islamiyati A, Sunusi N, Kalondeng A, Fatmawati F, Chamidah N. Use of two smoothing parameters in penalized spline estimator for bi-variate predictor non-parametric regression model. J Sci Islam Repub Iran. 2020;31(2):175–83.
 
  1. Saputra MD, Hadi AF, Riski A, Anggraeni D. Principal Component Regression in Statistical Downscaling with Missing Value for Daily Rainfall Forecasting. Int J Quant Res Model. 2021;2(3):139–46.
  2. Sari WJ, Wigena AH, Djuraidah A. Quantile regression with functional principal component in statistical downscaling to predict extreme rainfall. Int J Ecol Econ Stat. 2017;38(1):1–9.
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