A real-time prediction method for evaporation duct height based on historical meteorological data

To achieve rapid and accurate prediction of evaporation duct height (EDH) over the South China Sea, this paper proposes a real-time prediction method based on historical meteorological data. The method consists of a two-stage framework combining a time-series forecasting module and a physics-informed diagnostic module. A hybrid optimizer integrating northern goshawk optimization (NGO) and Bayesian optimization (BO), termed NGO-BO, is used to automatically tune the hyperparameters of the Informer model for high-precision meteorological forecasting. The physical equations of the classical NPS model are then embedded into the loss function of a physics-informed neural network (PINN) to construct the PINN-NPS diagnostic model, which estimates EDH using the predicted meteorological parameters. Experiments on meteorological data from Yongxing Island in the South China Sea show that the proposed NGO-BO-Informer achieves a mean absolute error (MAE) of 0.45 m and a coefficient of determination (R²) of 0.99, while the PINN-NPS model attains an MAE of 1.32 m and an R² of 0.73. The results confirm the effectiveness and practical value of the proposed approach for real-time EDH prediction under complex marine boundary layer conditions, providing useful references for maritime communication, radar warning, and electromagnetic environment monitoring.