A hybrid BiLSTM-XGBoost model for short-term forecasting of key parameters in nuclear power systems

reaches 0.9994), with substantial reductions in MSE, RMSE and MAE. The model effectively captures both abrupt transient changes and gradual parameter trends in LOCA scenarios, providing a robust approach for nuclear power plant transient parameter prediction, critical technical support for nuclear power operational safety and intelligent supervisory control, and a generalizable solution for forecasting high-dimensional, time-dependent nonlinear parameters in other industrial systems.