Artificial-Intelligence-Based Model for Early Strong Wind Warnings for High-Speed Railway System

Wind speed prediction (WSP) provides future wind information and is crucial for ensuring the safety of high-speed railway systems (HSRs). However, the accurate prediction of wind speed (WS) remains a challenge due to the nonstationary and nonlinearity of wind patterns. To address this issue, a novel artificial-intelligence-based WSP model (EE-VMD-TCGRU) is proposed in this paper. EE-VMD-TCGRU combines energy-entropy-guided variational mode decomposition (EE-VMD) with a customized hybrid network, TCGRU, that incorporates a novel loss function: the Gaussian kernel mean square error (GMSE). Initially, the raw WS sequence is decomposed into various frequency-band components using EE-VMD. TCGRU is then applied for each decomposed component to capture both long-term trends and short-term fluctuations. Furthermore, a novel loss function, GMSE, is introduced to the training of TCGRU to analyze the WS’s nonlinear patterns and improve prediction accuracy. Experiments conducted on real-world WS data from the Beijing–Baotou railway demonstrate that EE-VMD-TCGRU outperforms benchmark models, achieving a mean absolute error (MAE) of 0.4986, a mean square error (MSE) of 0.4962, a root mean square error (RMSE) of 0.7044, and a coefficient of determination (R2) of 94.58%. These results prove the efficacy of EE-VMD-TCGRU in ensuring train operation safety under strong wind environments.