Assessing trends and forecasting meteorological drought in South Africa using Savitzky–Golay enhanced hybrid deep learning

Drought constitutes one of the most significant natural hazards worldwide, exacerbated by climate variability and change, with profound implications for ecosystems, agriculture, and livelihoods. In South Africa, particularly within the drought-prone uMkhanyakude District of KwaZulu-Natal, comprehending rainfall variability and enhancing drought prediction are imperative for sustainable water and food security planning. This study utilized daily rainfall records from six meteorological stations spanning the years 1980 to 2023 to calculate the Standardized Precipitation Index (SPI) at 6-, 9-, and 12-month time scales. Long-term drought trends were evaluated employing Innovative Trend Analysis (ITA) methods, which identified statistically significant decreasing trends at five stations and an increasing trend at Riverview. To augment drought forecasting, a novel hybrid model that integrates the Savitzky-Golay filter with a Temporal Convolutional Network and Long Short-Term Memory (SG-TCN-LSTM) was developed. Comparative assessments against ARIMA, LSTM, TCN, and other hybrid models demonstrated that the SG-TCN-LSTM consistently achieved the lowest Root Mean Square Error (RMSE) values (0.0349-0.1453) and the highest [Formula: see text] values (0.95-0.99) across all SPI scales, indicating superior predictive accuracy and stability. The integration of signal smoothing with deep learning methodologies enhanced the robustness of forecasts, providing critical insights for proactive drought risk management. This research underscores the potential of hybrid models as reliable early-warning instruments for meteorological drought and establishes a framework that can inform national adaptation strategies. Future research should aim to extend the model to incorporate additional climatic drivers, evaluate its transferability across diverse climatic regions, and investigate its application in operational drought early-warning systems.