Enhanced reservoir outflow prediction using STL-Informer model: a decomposition–prediction–reconstruction framework

Accurate reservoir outflow prediction is crucial for water resource management. Although seasonal decomposition has shown good performance in time series prediction, its application in reservoir outflow prediction remains limited. This study proposes a decomposition–prediction–reconstruction framework for reservoir outflow prediction, integrating the STL (Seasonal and Trend decomposition using LOESS) method with the Informer model. The framework comprises three steps: first, decomposing complex reservoir outflow time series into trend, seasonal, and residual components using STL; then applying the Informer model to predict each component separately; and finally, reconstructing the final prediction by summing all components. Using 64-year data (1958–2021) from the Lianghekou Reservoir in the Yalong River (China) as a case study, STL-Informer was compared with five baseline methods, including extreme gradient boosting and long short-term memory (both with and without STL integration). Results demonstrate that STL decomposition significantly improves all baseline models’ performance, with enhancement ratios ranging from 27.35 to 88.04% across different metrics. STL-Informer achieves the best performance (NSE = 0.8650), showing 48.21% improvement in NSE, 43.05% reduction in RMSE, and 45.09% reduction in MAE compared with the baseline Informer. This study provides an effective technical approach for reservoir outflow prediction with significant value for reservoir operation management.