A Multi-Input Multi-Output Mapping Model for Multi-Reservoir Joint Operation Based on SSA-Optimized CNN-BiLSTM

Multi-reservoir joint operation is a high-dimensional, nonlinear, multi-input multi-output (MIMO) water-allocation problem. This study aimed to map the state variables of multiple water sources directly to the water allocated to each user with a single end-to-end model. A model combining a one-dimensional convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM) and the Sparrow Search Algorithm (SSA) was developed: the CNN extracted local coupling features along an ordered source–user feature vector, the BiLSTM captured temporal dependencies within a historical window, and the SSA jointly tuned the hidden-unit number, the initial learning rate and the L2 coefficient. The model was applied to monthly operation records of the Hongze Lake–Luoma Lake–Yangtze Diversion system in the Eastern Route of China’s South-to-North Water Diversion Project. On the held-out test period, the model attained an average R2 of 0.8637 and an average MAPE of 20.61%; the per-output test R2 values were 0.9403 (Hongze Lake supply), 0.8114 (Luoma Lake supply) and 0.8393 (Yangtze diversion supply). Relative to a plain BiLSTM, the full model raised R2 from 0.7763 to 0.8637 and reduced RMSE by 21.9%. Predictability differed among the three outputs, and the Luoma Lake output showed mild overfitting; a two-stage physical-constraint correction kept the allocations within the available-supply bound. The model provides a quantitative data-driven reference for multi-source water-allocation analysis and is intended as the mapping-simulation component of a closed-loop scheduling framework to be developed in future work, rather than as a stand-alone operational decision maker.