Multi-Task Learning Framework for Reservoir Water Temperature Field Prediction Using Modal Decomposition and Federated Learning
Year: 2026
Type: article
Abstract: Reservoir thermal stratification affects withdrawal temperatures and downstream aquatic ecosystems, making water temperature prediction important for sustainable reservoir operation. However, predicting two-dimensional water temperature fields across different operating conditions remains challenging. This study proposes a framework integrating proper orthogonal decomposition (POD), mutual information, dual-channel BiLSTM, bidirectional residual cross-attention, multi-task learning, and personalized federated learning. POD reduces field dimensionality, while multi-task regression heads directly predict modal coefficients for different forecast days. To account for differences among operating conditions, feature-extraction networks remain local, and only regression-network parameters are aggregated. Experiments were conducted using EFDC-simulated temperature fields from 75 operating conditions of Wudongde Reservoir. The model achieved MAE and RMSE values of 0.120 °C and 0.183 °C for 2-day forecasts and 0.314 °C and 0.449 °C for 10-day forecasts, respectively. Compared with full-model aggregation, theoretical parameter communication was reduced by 96.29%, with slightly higher prediction errors. The predicted temperature fields may support the assessment of selective-withdrawal conditions and downstream ecological temperature requirements, contributing to sustainable reservoir management.
Source: Sustainability
DOI: 10.3390/su182211450
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7225748815