Logo PTI Logo FedCSIS

Communication Papers of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 41

Prediction of Temperature and Precipitation Changes for Serbia Using Time Series Models with Machine Learning

, , , ,

DOI: http://dx.doi.org/10.15439/2024F2570

Citation: Communication Papers of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 41, pages 177–184 ()

Full text

Abstract. In the past few decades, there has been an evident change in climatic conditions worldwide as well as on the territory of Serbia. Extremely high temperatures, heavy floods, and sudden changes in the weather are increasingly frequent occurrences that bring great social and material damage. Climate change affects many economic sectors, like tourism and agriculture, which are potentially at risk. In Serbia, one of the vital economic sectors is agriculture. In order to act preventive, the main goal of this research was to predict the mean monthly temperature and precipitation for Serbia for periods 2021-2050 and 2071-2100. We collected a dataset titled ERA5 monthly averaged data on single levels from 1940 to present from the Climate Data Store. The dataset was analyzed and prepared to be used with SARIMA(X) and ARIMA(X) methods, which are utilized for prediction. The results that we identified are presented in this paper.

References

  1. S. Skendžić, M. Zovko, I. P. Živković, V. Lešić, and D. Lemić, “The Impact of Climate Change on Agricultural Insect Pests,” Insects, vol. 12, no. 5, Art. no. 440, 2021, http://dx.doi.org/http://dx.doi.org/10.3390/insects12050440.
  2. P. Radovan, “Razvojni problemi i prioriteti poljoprivrede Republike Srbije,” in Performanse i razvojne mogućnosti agrarne industrije u AP Vojvodini : prilog regionalnoj strategiji unapređenja agrarnog biznisa, vol. 15, Subotica, Serbia: Univerzitet u Novom Sadu, Subotica: Ekonomski fakultet, 2014, pp. 29–66. http://dx.doi.org/http://dx.doi.org/10.2298/PRMAIV2013029P.
  3. Statistical Office of the Republic of Serbia, “Statistical release | Gross Domestic Product, 2022.” Accessed: May 25, 2024. [Online]. Available: https://www.stat.gov.rs/en-US/vesti/statisticalrelease/?p=13820
  4. S. Đuričin, I. Beraha, and M. Đukić, “The impact of climate extremes on agricultural output in the Republic of Serbia,” Ecol. Nauka Privreda Iskustva, vol. 20, no. 72, Art. no. 72, 2013.
  5. V. Trkulja, A. Tomić, T. Popović, and R. Iličić, “The Impact of Climate Change on the Occurrence of Diseases and Pests of Agricultural Plants and Forest Trees,” in Natural Resources Management in a Changing Climate, vol. 8, 2023. http://dx.doi.org/http://dx.doi.org/10.7251/EORU2308477T.
  6. R. Jevtić, S. Maširević, and D. Vajgand, “The impact of climate change on diseases and pests of small grains and sunflower in the Vojvodina Region (Serbia). In Essays on Fundamental and Applied Environmental Topics,” in Essays on Fundamental and Applied Environmental Topics, New York, NY: Nova Science Pub Inc, 2012, pp. 277–306.
  7. J. Stypułkowska, “The Use of AI to Determine the Condition of Corn in a Field Robot that Meets the Requirements of Precision Farming,” in Annals of Computer Science and Information Systems, 2023, pp. 319–327. http://dx.doi.org/http://dx.doi.org/10.15439/2023F3649.
  8. M. Ruml et al., “On the use of regional climate models: Implications of climate change for viticulture in Serbia,” Agric. For. Meteorol., vol. 158–159, pp. 53–62, 2012, http://dx.doi.org/http://dx.doi.org/10.1016/j.agrformet.2012.02.004.
  9. D. T. Mihailović, B. Lalić, N. Drešković, G. Mimić, V. Djurdjević, and M. Jančić, “Climate change effects on crop yields in Serbia and related shifts of Köppen climate zones under the SRES-A1B and SRES-A2,” Int. J. Climatol., vol. 35, no. 11, pp. 3320–3334, 2015, http://dx.doi.org/http://dx.doi.org/10.1002/joc.4209.
  10. S. Orlović, Z. Galić, S. Stojnić, and B. Klašnja, “Monitoring of forest ecosystems in Serbia,” in Essays on Fundamental and Applied Environmental Topics, New York, NY: Nova Science Pub Inc, 2012, pp. 253–276.
  11. D. B. Stojanović et al., “Climate change within Serbian forests: Current state and future perspectives,” Topola, no. 208, pp. 39–56, 2021, http://dx.doi.org/http://dx.doi.org/10.5937/topola2108039S.
  12. “Climate change 2014: impacts, adaptation, and vulnerability: Working Group II contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change,” Cambridge University Press, New York, NY, 2014.
  13. “Climate change 2007: the physical science basis: contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,” Cambridge University Press, Cambridge ; New York, 2007.
  14. B. Lalic, J. Eitzinger, D. T. Mihailovic, S. Thaler, and M. Jancic, “Climate change impacts on winter wheat yield change – which climatic parameters are crucial in Pannonian lowland?,” J. Agric. Sci., vol. 151, no. 6, pp. 757–774, 2013, http://dx.doi.org/http://dx.doi.org/10.1017/S0021859612000640.
  15. D. Bozanic and D. Mitrovic, “Study on the Socio-economic Aspects of Climate Change in the Republic of Serbia,” United Nations Development Programme, 2019.
  16. V. M. Cvetković, S. Z. Vučić, and J. Gačić, “Climate change and national defense,” Vojno Delo, vol. 67, no. 5, pp. 181–203, 2015, http://dx.doi.org/http://dx.doi.org/10.5937/vojdelo1505181C.
  17. G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, “Predictability of monthly temperature and precipitation using automatic time series forecasting methods,” Acta Geophys., vol. 66, no. 4, pp. 807–831, 2018, http://dx.doi.org/http://dx.doi.org/10.1007/s11600-018-0120-7.
  18. H. do N. Camelo, P. S. Lucio, J. B. V. Leal Junior, P. C. M. de Carvalho, and D. von G. dos Santos, “Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks,” Energy, vol. 151, pp. 347–357, 2018, http://dx.doi.org/http://dx.doi.org/10.1016/j.energy.2018.03.077.
  19. G. K. Venayagamoorthy, K. Rohrig, and I. Erlich, “One Step Ahead: Short-Term Wind Power Forecasting and Intelligent Predictive Control Based on Data Analytics,” IEEE Power Energy Mag., vol. 10, no. 5, pp. 70–78, 2012, http://dx.doi.org/http://dx.doi.org/10.1109/MPE.2012.2205322.
  20. A. Kržič, I. Tošić, V. Djurdjević, K. Veljović, and B. Rajković, “Changes in climate indices for Serbia according to the SRES-A1B and SRES-A2 scenarios,” Clim. Res., vol. 49, pp. 73–86, 2011, http://dx.doi.org/http://dx.doi.org/10.3354/cr01008.
  21. F. Giorgi and L. O. Mearns, “Introduction to special section: Regional Climate Modeling Revisited,” J. Geophys. Res. Atmospheres, vol. 104, no. D6, pp. 6335–6352, 1999, http://dx.doi.org/http://dx.doi.org/10.1029/98JD02072.
  22. V. Djurdjević and B. Rajković, “Development of the EBU-POM coupled regional climate model and results from climate change experiments,” in Advances in Environmental Modeling and Measurements, UK ed. edition., New York, NY: Nova Science Pub Inc, 2010, pp. 23–32.
  23. A. Vukovic et al., “Global warming impact on climate change in Serbia for the period 1961-2100,” Therm. Sci., vol. 22, no. 6 Part A, pp. 2267–2280, 2018, http://dx.doi.org/http://dx.doi.org/10.2298/TSCI180411168V.
  24. M. Gocic and S. Trajkovic, “Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia,” Glob. Planet. Change, vol. 100, pp. 172–182, 2013, http://dx.doi.org/http://dx.doi.org/10.1016/j.gloplacha.2012.10.014.
  25. D. Burić, J. Mihajlović, V. Ducić, M. Milenković, and G. Anđelković, “Contribution to the study of climate change in Serbia using continentality, oceanity, and aridity indices,” Időjárás, vol. 127, no. 3, pp. 379–399, 2023, http://dx.doi.org/http://dx.doi.org/10.28974/idojaras.2023.3.6.