A hybrid intelligent algorithm for deterministic and probabilistic interval predictions of a high-speed railway wind warning system

A wind warning system (WWS) is designed to ensure the operational safety of trains amid various wind environments. The system integrates the Internet, cloud computing, and virtual instrumentation technologies to automatically collect wind speed data and intelligently output alert information. To precisely predict the wind speed, a hybrid intelligent algorithm termed VMD–SSA–GRU comprising variational mode decomposition (VMD), sparrow search algorithm (SSA), and gated recurrent unit (GRU) is proposed and integrated into the WWS. Besides, the quantile regression (QR) is applied to wind speed uncertainty estimation, and the probability density functions of partial intervals are further evaluated by an improved kernel density estimation method. Two typical wind speed datasets (typhoon and normal wind conditions) measured by the WWS are used for validation of the applicability of the developed forecasted model. Through comparison to seven single and four combined models, the developed model presents the highest forecast accuracy in both definitive and uncertainty predictions even in typhoon wind conditions. The study demonstrates that the WWS integrated with the proposed hybrid intelligent algorithm can provide sensible warning for the safety of train operation.