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Short-Term Strong Wind Risk Prediction for High-Speed Railway

Running at a fast speed, the high-speed train is prone to be interrupted by the surrounding strong wind. To ensure the safety of the trains, an effective approach is to deploy anemometers alongside the railway, such that the real-time and short-term predicted wind speed can be reported, and be further used by dispatchers to take protective actions in advance. However, in certain situations, the solely predicted wind speed is not informative enough to describe the wind status. It is difficult to tell if a strong wind incident could happen when the predicted wind speed is slightly lower than the strong wind threshold. We take the first attempt to predict the strong wind risk alongside the high-speed railway (HSR). A new model, called Multiple Attention Layer based Multi-Instance Learning (MAL-MIL), is proposed to address this problem. The key idea is to estimate the possibility that the actual wind speed exceeds the threshold conditionally on the predicted wind status. Based on attention mechanisms and long-short term memory network, the model can firstly generate deep representations of the future wind status. Then, though there is a lack of the risk ground truth, the multi-instance learning process facilitates the training procedure so that the relationships between these deep representations and the strong wind incidents could be quantified. Furthermore, considering the practicality of the model, we also design a result justification module to explain the reported risk. The superior performance is finally verified based on a real-world dataset.