Time series analysis and prediction of climate variables of Southern Java waters using support vector regression

• Autorzy: Satriadi Alfi , Handoyo Gentur

Identyfikatory

DOI

10.12912/27197050/195797

• Języki publikacji: EN

Abstrakty

EN

Southern Java Waters contribute significantly to aquatic economy of Indonesia by providing abundant fisheries resources. The understanding of sea state of the waters become a focal point. The aims of this paper are to analyze and to predict time series dataset comprising climate variables such as wind speed, surface temperature (SST), precipitation, and surface pressure of Southern Java Waters. The analysis has been done by decomposing the time series dataset to its patterns, trend and seasonality, and calculating the correlation matrix of the dataset. The prediction method employs support vector regression (SVR) algorithm. The performance of the resulted models is computed using mean squared error (MSE). The result shows that wind speed of Southern Java Waters is positively corelated with surface pressure and negatively corelated with SST and total precipitation. The lowest MSE occurs in SST model. Meanwhile, the largest MSE is the total precipitation model. The developed models could be used as prediction tools of climate variables for following years in the Southern Java Waters.

Słowa kluczowe

EN

wind speed; precipitation; support vector regression; sea surface temperature; surface pressure

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2025
• Tom: Vol. 26, iss. 1
• Strony: 177--186
• Opis fizyczny: Bibliogr. 39 poz., rys., tab.

Twórcy

autor

Satriadi Alfi

• Department of Oceanography, Faculty of Fisheries and Marine Science, Universitas Diponegoro, Semarang 50275, Indonesia

• https://orcid.org/0000-0002-4805-6316

autor

Handoyo Gentur

• Department of Oceanography, Faculty of Fisheries and Marine Science, Universitas Diponegoro, Semarang 50275, Indonesia

• https://orcid.org/0000-0002-3538-1611

Bibliografia

• 1. Budiman, A.S., Bengen, D.G., Nurjaya, I.W., Arifin, Z. and Ismail, M.F.A. (2022). A comparison of the three upwelling indices in the South Java sea shelf. CMU J. Nat. Sci, 21(3), p.e2022044.
• 2. Chang, C.C. and Lin, C.J. (2011). LIBSVM: a li­brary for support vector machines. ACM transac­tions on intelligent systems and technology (TIST), 2(3), 1–27.
• 3. Dash, R.K., Nguyen, T.N., Cengiz, K. and Sharma, A. (2023). Fine-tuned support vector regression model for stock predictions. Neural Computing and Applications, 35(32), 23295–23309.
• 4. Docquier, D., Vannitsem, S. and Bellucci, A. (2023). The rate of information transfer as a measure of ocean–atmosphere interactions. Earth System Dy­namics, 14(3), 577–591.
• 5. Drucker, H., Burges, C.J., Kaufman, L., Smola, A. and Vapnik, V. (1996). Support vector regression machines. Advances in neural information processing systems, 9.
• 6. Fanin, T. and Van Der Werf, G.R. (2017). Precipita­tion–fire linkages in Indonesia (1997–2015). Bio­geosciences, 14(18), 3995–4008.
• 7. Feng, Z.K., Niu, W.J., Tang, Z.Y., Jiang, Z.Q., Xu, Y., Liu, Y. and Zhang, H.R. (2020). Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. Journal of Hydrology, 583, 124627.
• 8. Hadi, S., Ningsih, N.S., Baskoro, M.S., Wirasatriya, A. and Kuswardani, A.R. (2020), July. The classi­fication of upwelling indicators base on sea surface temperature, chlorophyll-a and upwelling index, the case study in Southern Java to Timor Waters. In IOP Conference Series: Earth and Environmental Science 530(1), 012020. IOP Publishing.
• 9. Harris, C.R., Millman, K.J., Van Der Walt, S.J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N.J. and Kern, R. (2020). Array programming with NumPy. Nature, 585(7825), 357–362.
• 10. Hendon, H.H. (2003). Indonesian rainfall variabil­ity: Impacts of ENSO and local air–sea interaction. Journal of Climate, 16(11), 1775–1790.
• 11. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D. and Simmons, A. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 999–2049.
• 12. Horii, T., Siswanto, E., Iskandar, I., Ueki, I. and Ando, K. (2022). Can coastal upwelling trigger a climate mode? a study on intraseasonal‐scale coastal upwelling off java and the indian ocean dipole. Geophysical Research Letters, 49(15), p.e2022GL098733.
• 13. Hoyer, S. & Hamman, J. (2017). Xarray: N-D la­beled Arrays and Datasets in Python. Journal of Open Research Software, 5(1), 10.
• 14. Hunter, J.D. (2007). Matplotlib: A 2D Graphics En­vironment. Computing in Science & Engineering, 9(3), 90–95.
• 15. Iskandar, M.R., Ismail, M.F.A., Arifin, T. and Chan­dra, H. (2021). Marine heatwaves of sea surface temperature off south Java. Heliyon, 7(12).
• 16. Iwabuchi, B.L. and Gosselin, L. A. (2019). Long-term trends and regional variability in extreme temperature and salinity conditions experienced by coastal marine organisms on Vancouver Island, Can­ada. Bulletin of Marine Science, 95(3), 337–354.
• 17. Kuswanto, H. and Ningtyas, N.N.A., (2016), Oc­tober. Composite maps of surface pressure and geopotential to predict extreme high temperature in Surabaya. In 2016 12th International Conference on Mathematics, Statistics, and Their Applications (ICMSA) 139–143. IEEE.
• 18. Liao, Z., Dai, S. and Kuosmanen, T. (2024). Con­vex support vector regression. European Journal of Operational Research, 313(3), 858–870.
• 19. Liu, F., Song, F. and Luo, Y., (2024). Human-induced intensified seasonal cycle of sea surface temperature. Nature Communications, 15(1), 3948.
• 20. Met Office, U.K. (2010). Cartopy: A cartographic py­thon library with a matplotlib interface. Exeter, Devon.
• 21. Mondal, S., Lee, M.A., Wang, Y.C. and Semedi, B., (2022). Long-term variation of sea surface tem­perature in relation to sea level pressure and surface wind speed in southern Indian Ocean. Journal of Marine Science and Technology, 29(6), 784–793.
• 22. Muhammad, F.R. and Lubis, S.W. (2022). Impacts of the boreal summer intraseasonal oscillation on precipitation extremes in Indonesia. International Journal of Climatology, 43(PNNL-SA-177965).
• 23. Mutaqin, B.W. and Ningsih, R.L. (2023). Tidal char­acteristics in the Southern Waters of Java-Indonesia. Jurnal Geografi, 15(2), 154–164.
• 24. Nugroho, B.D.A. (2015). Relationships between Sea Surface Temperature (SST) and rainfall distri­bution pattern in South-Central Java, Indonesia. The Indonesian Journal of Geography, 47(1), 20.
• 25. Nurlatifah, A., Susanti, I. and Suhermat, M. (2021). Variability and trend of sea level in southern waters of Java, Indonesia. Journal of Southern Hemisphere Earth Systems Science, 71(3), 272–283.
• 26. Parbat, D. and Chakraborty, M. (2020). A python based support vector regression model for predic­tion of COVID19 cases in India. Chaos, Solitons & Fractals, 138, 109942.
• 27. Ramadhan, R., Marzuki, M., Suryanto, W., Sholihun, S., Yusnaini, H., Muharsyah, R. and Hanif, M. (2022). Trends in rainfall and hydrometeorological disasters in new capital city of Indonesia from long-term sat­ellite-based precipitation products. Remote Sensing Applications: Society and Environment, 28, 100827.
• 28. Scrosati, R.A. (2020). Upwelling spike and marked SST drop after the arrival of cyclone Dorian to the Atlantic Canadian coast. Journal of Sea Research, 159, 101888.
• 29. Singh, V.K. and Roxy, M.K. (2022). A review of ocean-atmosphere interactions during tropical cy­clones in the north Indian Ocean. Earth-Science Reviews, 226, 103967.
• 30. Sofiati, I. and Putranto, M.F. (2020), September. The analysis of tropical cyclones that occurred in the southern sea of Java during the period 2004-2019 and their effects on sea-atmospheric conditions. In IOP Conference Series: Earth and Environmental Science 572, 1, 012032). IOP Publishing.
• 31. Sofiati, I., Yulihastin, E., Suaydhi, S. and Putranto, M.F. (2020). Meridional variations of sea surface temperature and wind over southern sea of Java and its surroundings. Journal of Physics and Its Applica­tions, 3(1), 129–135.
• 32. Teng, T.P. and Chen, W.J. (2024). Using Pearson correlation coefficient as a performance indicator in the compensation algorithm of asynchronous temperature-humidity sensor pair. Case Studies in Thermal Engineering, 53, 103924.
• 33. Thomas, S., Pillai, G.N. and Pal, K. (2017). Pre­diction of peak ground acceleration using ϵ-SVR, ν-SVR and Ls-SVR algorithm. Geomatics, Natural Hazards and Risk, 8(2), 177–193.
• 34. Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.
• 35. Wang, C. (2019). Three-ocean interactions and cli­mate variability: a review and perspective. Climate Dynamics, 53(7–8), 5119–5136.
• 36. Wen, C., Wang, Z., Wang, J., Li, H., Shi, X., Gao, W. and Huang, H. (2023). Variation of the coastal upwelling off South Java and their impact on lo­cal fishery resources. Journal of Oceanology and Limnology, 1–16.
• 37. Wijaya, Y.J., Wisha, U.J., Rejeki, H.A. and Ismunar­ti, D.H. (2023). Variability of the South Java Current from 1993 to 2021, and its relationship to ENSO and IOD events. Asia-Pacific Journal of Atmospheric Sciences, 1–15.
• 38. Wirasatriya, A., Setiawan, J.D., Sugianto, D.N., Rosyadi, I.A., Haryadi, H., Winarso, G., Setiawan, R.Y. and Susanto, R.D. (2020). Ekman dynamics variability along the southern coast of Java revealed by satellite data. International Journal of Remote Sensing, 41(21), 8475–8496.
• 39. Zhang, F. and O’Donnell, L.J. (2020). Support vec­tor regression. In Machine learning. Academic Press.