FORECASTING RAINFALL IN PANGKALPINANG CITY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS (SARIMAX)

  • Ririn Amelia Mathematics Department, Faculty of Technic, Universitas Bangka Belitung
  • Elyas Kustiawan Mathematics Department, Faculty of Technic, Universitas Bangka Belitung
  • Ineu Sulistiana Mathematics Department, Faculty of Technic, Universitas Bangka Belitung
  • Desy Yuliana Dalimunthe Mathematics Department, Faculty of Technic, Universitas Bangka Belitung
DOI: https://doi.org/10.30598/barekengvol16iss1pp137-146
Keywords: forecasting, rainfall, sarima, sarimax

Abstract

Changes in extreme rainfall can cause disasters or losses for the wider community, so information about future rainfall is also needed. Rainfall is included in the category of time series data. One of the time series methods that can be used is Autoregressive Integrated Moving Average (ARIMA) or Seasonal ARIMA (SARIMA). However, this model only involves one variable without involving its dependence on other variables. One of the factors that can affect rainfall is wind speed which can affect the formation of convective clouds. In this study, the ARIMA model was expanded by adding eXogen variables and seasonal elements, namely the SARIMAX model (Seasonal ARIMA with eXogenous input). Based on the analysis that has been carried out, the prediction of rainfall in Pangkalpinang City, Bangka Belitung Islands Province can be modeled with the SARIMAX model (0,1,3)(0,1,1){12} for monthly rainfall and SARIMAX (0,1,2 )(0,1,3){12} for maximum daily rainfall. When compared with the actual data and previous studies using ARIMAX, the SARIMAX model is still better in the forecasting process when compared to the ARIMAX model. If viewed based on the AIC value of the SARIMA model, the SARIMAX model is also more suitable to be used to predict rainfall in Pangkalpinang City.

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References

BMKG, “BMKG Ingatkan Prospek Iklim 2021,” 2020. https://www.bmkg.go.id/press-release/?p=bmkg-ingatkan-prospek-iklim-2021&tag=press-release〈=ID.

Kompas.com, “Cuaca Ekstrem Diprediksi Landa Bangka Belitung Selama Tiga Hari ke Depan,” 2020. https://regional.kompas.com/read/2020/01/26/15181151/cuaca-ekstrem-diprediksi-landa-bangka-belitung-selama-tiga-hari-ke-depan?page=all (accessed Apr. 01, 2021).

Tempo.co, “Waspada! BMKG Tetpkan Babel Status Siaga,” 2021. https://nasional.tempo.co/read/1422975/waspada-bmkg-tetapkan-babel-status-siaga (accessed Mar. 30, 2021).

W. Teguh, “Peramalan iklim dan cuaca berbasis teknologi informasi,” no. March, 2020, [Online]. Available: https://www.researchgate.net/publication/339602291_Peramalan_Iklim_dan_Cuaca_Berbasis_Teknologi_Informasi.

S. Nurmita, Sugianto, and H. Wendi, “Analisa Arah Angin Terhadap Curah Hujan Menggunakan Equatorial Atmosphere Radar (EAR) dan Optical Rain Gauge (Org) Di Atas Kototabang Sumatera Barat,” [Online]. Available: http://repository.unri.ac.id/xmlui/handle/123456789/7888.

S. Renny Elfira Wulansari, “Peramalan Netflow Uang Kartal dengan Metode ARIMAX dan Radial Basis Function Network (Studi Kasus Di Bank Indonesia),” J. Sains dan Seni POMITS, vol. 3, no. 2, pp. 73–78, 2014.

M. L. Izza, D. Susilaningrum, and Suhartono, “… Penjualan Sepeda Motor Menurut Tipe dengan Pendekatan Autoregressive Integrated Moving Average with Exogeneous Input (ARIMAX) di Kabupaten Banyuwangi,” J. Sains Dan Seni Pomits, vol. 3, no. 2, pp. 176–181, 2014.

K. Wangdi, P. Singhasivanon, T. Silawan, S. Lawpoolsri, N. J. White, and J. Kaewkungwal, “Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: A case study in endemic districts of Bhutan,” Malar. J., vol. 9, no. 1, p. 251, Dec. 2010, doi: 10.1186/1475-2875-9-251.

Kongcharoen and Kruangpradit, “Autoregressive Integrated Moving Average with Exogenous Variable ( ARIMAX ) Model for Nigerian Non Oil Export,” Eur. J. Bus. Manag., vol. 8, no. 36, pp. 2010–2015, 2016.

A. R. Suryani, Sugiman, and P. Hendikawati, “Peramalan Curah Hujan Dengan Metode Autoregressive Integrated Moving Average With Exogenous Input (Arimax),” Unnes J. Math., vol. 7, no. 1, pp. 120–129, 2018.

D. Rosadi, Analisis Ekonometrika & Runtun Waktu Terapan dengan R. Yogyakarta: ANDI, 2011.

J. D. Cryer and K.-S. Chan, Time Series Analysis With Applications in R. Springer, 2008.

K. P. Chong, “Forecasting the Cocoa Black Pod Incidence in Sabah Using Arimax Model,” Malaysian Cocoa J., vol. 10, no. May 2018, pp. 39–48, 2018.

M. S. Hossain, S. Ahmed, and M. J. Uddin, “Impact of weather on COVID-19 transmission in south Asian countries: An application of the ARIMAX model,” Sci. Total Environ., vol. 761, p. 143315, Mar. 2021, doi: 10.1016/j.scitotenv.2020.143315.

Published
2022-03-21
How to Cite
[1]
R. Amelia, E. Kustiawan, I. Sulistiana, and D. Dalimunthe, “FORECASTING RAINFALL IN PANGKALPINANG CITY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS (SARIMAX)”, BAREKENG: J. Math. & App., vol. 16, no. 1, pp. 137-146, Mar. 2022.
Issue
Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

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