<![CDATA[Accurate temperature prediction is crucial for climate monitoring, energy management, and disaster preparedness. This study provides a comparative analysis of various machine learning models, including Random Forest, Gradient Boosting, Histogram-Based Gradient Boosting, XGBoost, Support Vector Regression (SVR), Ridge Regression, and Lasso Regression, to evaluate their predictive accuracy, stability, and generalization capability. The models are assessed using five-fold cross-validation, with the R² metric as the primary evaluation criterion. The results indicate that Random Forest achieves the highest accuracy, with an R² mean of 0.999994, demonstrating its strong ability to model temperature variations. Ridge Regression unexpectedly performs at a similar level, suggesting that the dataset contains strong linear dependencies. Gradient Boosting, Histogram-Based Gradient Boosting, and XGBoost also achieve high accuracy, confirming their effectiveness in capturing complex relationships between meteorological parameters. SVR, while effective, exhibits higher variance, indicating that it may require further tuning for improved consistency. Lasso Regression, with an R² mean of 0.9783, shows the lowest accuracy, confirming that linear models are less suitable for complex meteorological predictions. These findings highlight the superiority of ensemble-based methods in temperature forecasting, reinforcing their stability and adaptability. Future research should explore hybrid models that integrate ensemble techniques with feature engineering optimizations to further enhance predictive performance. This study contributes to the ongoing development of machine learning applications in meteorology, offering insights into model selection for climate-related forecasting tasks.]]>
Copyrights © 2025
