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

作者：Ying Chen, Huanping Wu, Nengfu Xie, Xiaohe Liang, Lihua Jiang, Minghui Qiu, Yonglei Li · 发表于：Earth Science Informatics · 年份：2025 · DOI：10.1007/s12145-025-01813-0 · 被引用次数：7 · 研究领域：Hydrology and Drought Analysis、Hydrological Forecasting Using AI、Climate variability and models

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 systems, and social stability. Advanced techniques such as machine learning and deep learning models outperform traditional models by improving meteorological 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 multivariate 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 captures 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 prediction (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 R $$^2$$ = 0.934 for SPEI-12).