Comparative Performance of LSTM, ANN, and GAM in Predicting Precipitation and Temperature Anomalies Under Accelerated Warming: Evidence from Thohoyandou, South Africa (1990–2025)

Abstract

Accurate forecasting of local weather patterns is essential for climate resilience and sustainable planning. This study analysed 35 years (1990–2025) of hourly temperature and precipitation data from Thohoyandou, South Africa, to assess the impacts of climate change and improve anomaly prediction. Exploratory analysis and Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) decomposition revealed accelerated warming trends of 0.025 °C per year in temperature anomalies, alongside highly irregular rainfall patterns characterised by extreme events rather than systematic changes. Three models, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and a Generalised Additive Model (GAM), were evaluated for anomaly forecasting, with feature selection guided by LASSO regression. For temperature, the LSTM performed better than the ANN and GAM, with MSE = 0.458, MAE = 0.457, MBE = 0.087, and MASE = 0.510. For temperature anomalies, the LSTM model performed best, followed by the GAM and ANN models. For precipitation anomalies, the LSTM model also achieved the lowest prediction error, with MSE = 0.187, MAE = 0.111, MBE = −0.009, and MASE = 1.873; however, MASE values above 1 indicate that rainfall forecasting remains challenging. These results show the LSTM model’s ability to handle temperature anomalies and the difficulty of modelling rainfall. GAM performed less accurately but steadily in modelling precipitation.