Site-Specific Deterministic Temperature and Humidity Forecasts with Explainable and Reliable Machine Learning

This study investigates the feasibility of optimizing site-specific temperature and dew point forecasts by adopting the gradient boosting decision tree model XGBoost, supported by insights from Shapley Additive Explanations (SHAP) to increase the reliability of the machine learning-based forecasts.