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Journal : Enthusiastic : International Journal of Applied Statistics and Data Science

Temperature Prediction in Norway Using GRUs: A Machine Learning Approach Andrie Pasca Hendradewa; Utari, Dina Tri
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 1, April 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss1.art2

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.