Rainfall prediction is still a difficult challenge because rainfall is nonlinear, intermittent, and highly variable, especially in semi-arid climates. Accurate rainfall prediction is crucial for water resource management, agricultural planning, climate-driven decision-making, and more. This study proposes a comparative framework based on machine learning and ensemble learning techniques to predict daily rainfall in Settat, Morocco, as a representative semi-arid region. Five predictive models were trained and evaluated based on meteorological station observations: Random Forest, XGBoost, LightGBM, CatBoost, and a Multilayer Perceptron (MLP). The models' performance was evaluated based on mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and the coefficient of determination (R-squared). The results demonstrate that the performance and stability of gradient boosting algorithms are superior to all other evaluated models. Specifically, LightGBM produced the fewest erroneous values and explained rainfall variability best. These results underscore the success of boosting-based ensemble techniques in modeling inconsistent precipitation patterns and provide a comparative framework for machine-learning-based rainfall forecasting in semi-arid environments.
Zemnazi et al. (2026) studied this question.