10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
Authors - Lillian Mzyece, Jackson Phiri, Mayumbo Nyirenda Abstract - Accurate rainfall forecasts are critical for various sectors, yet traditional methods struggle due to evolving and non-linear weather patterns. This study evaluates four machine learning algorithms—Support Vector Machines (SVM), Random Forest (RF), Neural Prophet (NP), and Long Short-Term Memory (LSTM)—to determine the most effective algorithm for rainfall forecasting in Zambia. Results show that Neural Prophet outperformed others, achieving the lowest RMSE (4.67), MAE (16.75), and MAPE (13.40%). Its autoregressive capabilities, interpretability, and reduced parameter complexity make Neural Prophet the preferred choice for forecasting rainfall trends in Zambia.
