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Abstract

Accurate temperature forecasting in Norway is significant for environmental stewardship and disaster management, in addition to providing essential support for critical sectors, including agriculture, urban development, and energy resource management. This study employed the gated recurrent unit (GRU) to augment the precision of temporal temperature forecasts. After that, it was used to project temperatures for seven days. The dataset, obtained from https://www.yr.no/nb, comprised records of minimum and maximum temperatures spanning from February 1, 2018, to December 31, 2024. The data was partitioned, with 80% allocated for training and 20% designated for testing. Utilizing a training regimen of 20 epochs alongside a three-day lookback interval, the model attained R² scores of 0.82 for minimum temperature predictions and 0.86 for maximum temperature forecasts. These results underscore the GRU model’s capacity to accurately capture daily temperature variations and produce dependable predictions. Given its commendable performance on training and testing datasets, the GRU model is particularly suitable for temperature forecasting.

Keywords

Daily Temperature GRU-Based Time Series Prediction Climate Pattern Analysis Norwegian Regional Temperature Prediction

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How to Cite
Andrie Pasca Hendradewa, & Utari, D. T. (2025). Temperature Prediction in Norway Using GRUs: A Machine Learning Approach. Enthusiastic : International Journal of Applied Statistics and Data Science, 5(1), 9–19. https://doi.org/10.20885/enthusiastic.vol5.iss1.art2
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References

  1. D.K. Roy, M.A. Hossain, M.P. Haque, A. Alataway, A.Z. Dewidar, and M.A. Mattar, “Automated model selection using Bayesian optimization and the asynchronous successive halving algorithm for predicting daily minimum and maximum temperatures,” Agriculture, vol. 14, no. 2, Feb. 2024, Art. no 278, doi: 10.3390/agriculture14020278.
  2. J. Oliver, “Norway’s climate odyssey: Assessing impacts, mitigation strategies, and policy responses in a warming Arctic,” Endless Int. J. Future Stud., vol. 6, no. 3, pp. 29–43, Sep. 2023, doi: 10.54783/endlessjournal.v6i3.198.
  3. F. Akcakoca and H. Apaydin, “Modelling of Bektas Creek daily streamflow with generalized regression neural network method,” Int. J. Adv. Sci. Res. Eng., vol. 6, no. 2, pp. 97–103, Feb. 2020, doi: 10.31695/IJASRE.2020.33717.
  4. F. Xie, H. Yan, Y. Long, H. Guo, H. Liu, and P. Yu, “Weather prediction based on multivariate LSTM neural network model,” in Intelligent Computing Technology and Automation (Advances in Transdisciplinary Engineering Series 47), Z. Hou, Ed., Amsterdam, Netherland: IOS Press BV, 2024, pp. 298–303, doi: 10.3233/ATDE231201.
  5. C. Wang, P. Wang, P. Wang, B. Xue, and D. Wang, “Using conditional generative adversarial 3-D convolutional neural network for precise radar extrapolation,” IEEE. J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 5735–5749, 2021, doi: 10.1109/JSTARS.2021.3083647.
  6. F. Wang, D. Zhang, G. Min, and J. Li, “Reservoir production prediction based on variational mode decomposition and gated recurrent unit networks,” IEEE Access, vol. 9, pp. 53317–53325, 2021, doi: 10.1109/ACCESS.2021.3070343.
  7. F.R. Alharbi and D. Csala, “Short-term wind speed and temperature forecasting model based on gated recurrent unit neural networks,” in 2021 3rd Global Power Energy Commun. Conf. (GPECOM), Oct. 2021, pp. 142–147, doi: 10.1109/GPECOM52585.2021.9587479.
  8. X. Wang et al., “Sparse data-extended fusion method for sea surface temperature prediction on the East China Sea,” Appl. Sci., vol. 12, no. 12, Jun. 2022, Art. no 5905, doi: 10.3390/app12125905.
  9. D.F. Godoy-Rojas et al., “Attention-based deep recurrent neural network to forecast the temperature behavior of an electric arc furnace side-wall,” Sensors, vol. 22, no. 4, Feb. 2022, Art. no 1418, doi: 10.3390/s22041418.
  10. T. Sivakumar, P.T. Suraj, and P.C. Jayashree, “Trends in climatic change in the last 50 years at Seven Agro-climatic regions of Tamil Nadu,” in Climate Change Modelling, Planning and Policy for Agriculture, A.K. Singh, J.C. Dagar, A. Arunachalam, and G.R.K Shelat (Eds.). New Delhi, India: Springer India, 2015, pp. 187–198, doi: 10.1007/978-81-322-2157-9_19.
  11. A. Panda and N. Sahu, “Trend analysis of seasonal rainfall and temperature pattern in Kalahandi, Bolangir and Koraput districts of Odisha, India,” Atmospheric Sci. Lett., vol. 20, no. 10, Oct. 2019, doi: 10.1002/asl.932.
  12. J. Abaurrea, J. Asín, and A.C. Cebrián, “Modelling the occurrence of heat waves in maximum and minimum temperatures over Spain and projections for the period 2031-60,” Glob Planet Change, vol. 161, pp. 244–260, Feb. 2018, doi: 10.1016/j.gloplacha.2017.11.015.
  13. D. O’Shaughnessy, “Trends and developments in automatic speech recognition research,” Comput. Speech Lang., vol. 83, p. 101538, Jan. 2024, doi: 10.1016/j.csl.2023.101538.
  14. J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” 2014, arXiv: 1412.3555v1.
  15. O. Kaypakli and M. Özgeyik, “The effect of heart rate and pulse pressure on mean arterial pressure: the combined formula for calculation of mean arterial pressure,” Blood Press. Monit., vol. 26, no. 5, pp. 373–379, Oct. 2021, doi: 10.1097/MBP.0000000000000548.
  16. A.F. Saad and Z.I. Elghobary, “Theoretical predictions of nuclear binding energy for the observed nuclei: the influence of coefficients and terms in a semi-empirical mass formula,” Phys. Scr., vol. 99, no. 8, Aug. 2024, Art. no 99 085308, doi: 10.1088/1402-4896/ad6198.
  17. Y. Sun, Y. Polyanskiy, and E. Uysal, “Sampling of the Wiener process for remote estimation over a channel with random delay,” IEEE Trans. Inf. Theory, vol. 66, no. 2, pp. 1118–1135, Feb. 2020, doi: 10.1109/TIT.2019.2937336.
  18. G. Hexner and H. Weiss, “An extended Kalman filter with a computed mean square error bound,” in 53rd IEEE Conf. Decis. Control, Dec. 2014, pp. 5008–5014, doi: 10.1109/CDC.2014.7040171.
  19. S. Kotaška, D. Duchan, P. Pelikán, and M. Špano, “Spectral analysis of oscillatory wind wave parameters in fetch-limited deep-water conditions at a small reservoir and their prediction: Case study of the Hulín Reservoir in the Czech Republic,” J. Hydrol. Hydromech., vol. 72, no. 1, pp. 95–112, Mar. 2024, doi: 10.2478/johh-2023-0042.
  20. N. Ichihara et al., “Achieving clinically optimal balance between accuracy and simplicity of a formula for manual use: Development of a simple formula for estimating liver graft weight with donor anthropometrics,” PLoS One, vol. 18, no. 1, Jan. 2023, Art. no e0280569, doi: 10.1371/journal.pone.0280569.
  21. L. Kusumawati, E. Setyowati, and A.B. Purnomo, “Practical-empirical modeling on envelope design towards sustainability in tropical architecture,” Sustainability, vol. 13, no. 5, Mar. 2021, Art. no 2959, doi: 10.3390/su13052959.