Learning Approaches for Weather Forecasting: A Comparative Study

This study investigates daily average air temperature forecasting using meteorological data collected in Istanbul, Türkiye, between January 2015 and December 2024. The dataset consists of 3,653 daily observations including temperature, precipitation, and wind-related variables. To improve forecasting performance, several preprocessing and feature engineering techniques were applied, including seasonal encoding, lagged temperature features, moving averages, and Min–Max normalization. The forecasting task was formulated as a multivariate time-series regression problem, and the temporal structure of the data was preserved through chronological train–test splitting.
Six prediction models representing statistical, machine learning, and deep learning approaches were evaluated: Linear Regression, Random Forest, Support Vector Regression (SVR), XGBoost, LightGBM, and Long Short-Term Memory (LSTM). Model performance was assessed using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). Experimental results showed that Linear Regression achieved the best overall performance with an RMSE of 0.0442 and an R² value of 0.9543. Random Forest produced comparable results with an RMSE of 0.0452 and an R² value of 0.9523. In contrast, SVR, XGBoost, LightGBM, and LSTM exhibited substantially lower predictive performance and negative R² values on the test dataset.
The findings indicate that daily temperature data in Istanbul exhibit strong temporal continuity and seasonality. Models capable of effectively utilizing lagged temperature information and seasonal patterns achieved the highest forecasting accuracy. The results also demonstrate that increasing model complexity does not necessarily improve predictive performance when working with relatively limited local meteorological datasets. Overall, the study highlights the effectiveness of simple and interpretable models for short-term temperature forecasting and provides a comparative evaluation of different machine learning approaches under real-world forecasting conditions.

forecasting, machine learning, time series analysis, deep learning, regression

1. Meteoroloji Genel Müdürlüğü, “Hava tahmini hakkında sıkça sorulan sorular,” Ankara, Türkiye, 2025. [Online]. Available: https://www.mgm.gov.tr (accessed: Dec. 2025). [Google Scholar] [Crossref]

2. Climavision, “The ultimate guide to weather forecast models,” 2025. [Online]. Available: https://www.climavision.com (accessed: Dec. 2025). [Google Scholar] [Crossref]

3. Meteoroloji Genel Müdürlüğü, “Meteoroloji nedir?,” Ankara, Türkiye, 2025. [Online]. Available: https://www.mgm.gov.tr (accessed: Dec. 2025). [Google Scholar] [Crossref]

4. J. A. Brotzge et al., “The evolution of numerical weather prediction and machine learning,” Bull. Amer. Meteorol. Soc., pp. E698–E705, 2023. [Google Scholar] [Crossref]

5. L. Chen et al., “Machine learning methods in weather and climate applications,” Int. J. Appl. Sci., 2023. [Google Scholar] [Crossref]

6. R. Gupta et al., “Comparative analysis of machine learning algorithms for temperature and rainfall prediction,” Environ. Model. Softw., vol. xx, pp. 105123, 2023. [Google Scholar] [Crossref]

7. H. Zhang et al., “Machine learning methods for weather forecasting,” Atmosphere, vol. 82, 2025. [Google Scholar] [Crossref]

8. P. R. Gangula et al., “A comparative study of machine learning techniques for nuanced weather prediction,” in Proc. Int. Conf. Comput. Sci. Comput. Intell. (CSCI), 2023. [Google Scholar] [Crossref]

9. A. Sevinç and B. Kaya, “Derin öğrenme yöntemleri ile sıcaklık tahmini: Diyarbakır ili örneği,” J. Comput. Sci., pp. 217–225, 2021. [Google Scholar] [Crossref]

10. S. Nigam et al., “Comparative study to determine accuracy for weather prediction using machine learning,” in Proc. Int. Conf. Comput. Commun. Inform. (ICCCI), 2023. [Google Scholar] [Crossref]

11. M. S. Rahman et al., “Comparative evaluation of weather forecasting using machine learning models,” in Proc. Int. Conf. Comput. Inf. Technol. (ICCIT), 2023. [Google Scholar] [Crossref]

12. K. B. Maheswari and S. Gomathi, “A comprehensive analysis of weather prediction using machine learning,” in Proc. Int. Conf. Sci. Technol. Eng. Math. (ICONSTEM), 2024. [Google Scholar] [Crossref]

13. P. Das et al., “An intelligent regression approach for weather forecasting system using machine learning,” in Proc. Int. Conf. Cogn. Green Ubiquitous Comput. (IC-CGU), 2024. [Google Scholar] [Crossref]

14. K. M. S. A. Hennayake et al., “Machine learning based weather prediction model for short term weather prediction in Sri Lanka,” in Proc. Int. Conf. Inf. Autom. Sustain. (ICIAfS), 2021. [Google Scholar] [Crossref]

• A Machine Learning Model for Predicting the Risk of Developing Diabetes - T2DM Using Real-World Data from Kilifi, Kenya
• AI-Powered Facial Recognition Attendance System Using Deep Learning and Computer Vision
• A Comprehensive Review on Brain Tumour Segmentation Using Deep Learning Approach
• A Scalable Retrieval-Augmented Generation Pipeline for Domain-Specific Knowledge Applications
• Predictive Maintenance in Semiconductor Manufacturing Using Machine Learning on Imbalanced Dataset