Solar radiation prediction in semi-arid regions: A machine learning approach and comprehensive evaluation in Gadarif, Sudan

Solar radiation (H) is a critical factor in Earth’s surface processes, influencing climate, ecosystems, agriculture, and energy fluxes. Accurate prediction of daily H is essential for advancing solar power as a sustainable energy source. This study evaluates the effectiveness of machine learning (ML) models-support vector regression (SVR), extreme gradient boosting (XGBoost), boosted regression forest (BRF), and k-nearest neighbors (K-NN)-in predicting daily H in Gadarif, Sudan, a semi-arid region with limited prior research on solar radiation. The models were developed using daily climatic variables, including temperature and a binary precipitation variable (Pt) to account for cloud cover effects. The dataset was split into training (80%) and testing (20%) subsets, with model performance evaluated using key metrics: coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). BRF achieved the best performance with an R² of 0.963 and RMSE of 4.38 (MJ m⁻² d⁻¹) during training. However, model performance decreased during testing, with XGBoost and K-NN showing higher error margins. Including Pt improved the models’ ability to account for cloud cover effects, particularly on overcast days. Despite these improvements, challenges remained in predicting H under extreme climatic conditions, highlighting the need for more advanced approaches. These findings suggest that ML models can be effectively adapted for H prediction in other semi-arid and arid regions. The results underscore the importance of considering precipitation and cloud cover in H predictions, which is crucial for optimizing solar energy systems and enhancing agricultural planning.