Rainfall Prediction in South Africa Using Time‐Series Analysis

Predicting rainfall is challenging, especially in areas with highly fluctuating rainfall, like South Africa. Precise forecasting is crucial for agriculture, water management, and disaster response industries. This research focuses on developing a robust and accurate rainfall prediction model specifically for South Africa, employing time‐series analysis techniques, namely autoregressive integrated moving average (ARIMA), Facebook Prophet, and long short‐term memory (LSTM) neural networks. The study uses a dataset of historical rainfall records from several South African provinces that experience heavy precipitation. The models are evaluated based on performance metrics like mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and R ‐squared ( R 2 ). The findings reveal that the enhanced ARIMA(2, 1, 2) model surpasses the LSTM and Facebook Prophet models, attaining strong statistical performance (RMSE of 0.1793 and R 2 score of 0.86), indicative of perfect prediction accuracy. Conversely, LSTM and Prophet exhibited higher error rates and broader prediction intervals, highlighting their limitations in managing the complex and highly variable rainfall data and emphasizing the need for further investigation and the inclusion of additional predictors. This study advances time‐series forecasting methods and offers valuable insights for improving rainfall prediction accuracy in intricate and unpredictable settings.