Harnessing Deep Learning for Meteorological Drought Forecasts in the Northern Cape, South Africa

Abstract

The National Disaster Management Center has declared a drought disaster in the Northern Cape, South Africa, due to persistent dry conditions that impact regions such as the Western, Eastern, and Northern Cape provinces. Accurate drought predictions are vital for decision-making and planning in vulnerable areas. This study introduces a hybrid intelligence model, combining long short-term memory (LSTM) and convolutional neural networks (CNNs), to forecast short-term meteorological droughts using the Standardized Precipitation Evapotranspiration Index (SPEI). Applied to Kimberley and Upington in the Northern Cape, the model predicts 1-month and 3-month SPEI indices (SPEI-1 and SPEI-3). The hybrid model’s performance, compared to benchmark models such as artificial neural networks (ANNs), LSTM, and CNN, is measured through statistical analysis. In Kimberley, the CNN–LSTM model displayed a robust positive correlation of 0.901573 and a low mean absolute error (MAE) of 0.082513. Similarly, in Upington, the CNN–LSTM model exhibited strong performance, achieving a correlation coefficient of 0.894805 and a MAE of 0.085212. These results highlight the model’s remarkable precision and effectiveness in predicting drought conditions in both regions, underscoring its superiority over other forecasting techniques. SPEI, incorporating potential evapotranspiration and rainfall, is superior for drought analysis amidst climate change. The findings enhance understanding of drought patterns and aid mitigation efforts. The CNN–LSTM hybrid model demonstrated noteworthy results, outperforming ANN, CNN, and LSTM, emphasizing its potential for precise meteorological drought predictions.