STAT-LSTM: A multivariate spatiotemporal feature aggregation model for SPEI-based drought prediction

In recent decades, shifts in the spatiotemporal patterns of precipitation and extreme temperatures have contributed to more frequent droughts. These changes impact not only agricultural production but also food security, ecological sys- tems, and social stability. Advanced techniques such as machine learning and deep learning models outperform traditional models by improving meteorolog- ical drought prediction. Specifically, this study proposes a novel model named the multivariate feature aggregation-based temporal convolutional network for meteorological drought spatiotemporal prediction (STAT-LSTM). The method consists of three parts: a feature aggregation module, which aggregates multi- variate features to extract initial features; a self-attention-temporal convolutional network (SA-TCN), which extracts time series features and uses the self-attention module’s weighting mechanism to automatically capture global dependencies in the sequential data; and a long short-term memory network (LSTM), which cap- tures long-term dependencies. The performance of the STAT-LSTM model was assessed and compared via performance indicators (i.e., MAE, RMSE, and R ² ). The results indicated that STAT-LSTM provided the most accurate SPEI pre- diction (MAE = 0.474, RMSE = 0.63, and R ² = 0.613 for SPEI-3; MAE = 0.356, RMSE = 0.468, and R ² = 0.748 for SPEI-6; MAE = 0.284, RMSE = 0.437, and R ² = 0.813 for SPEI-9; and MAE = 0.182, RMSE = 0.267, and R2 = 0.934 for SPEI-12).