基于LSTM的高铁大风预测模型及算法研究

通过对风速数据进行时间序列分析,建立风速预测模型,实现大风灾害的预警,对提升高铁运营安全保障能力具有重要意义。通过分析某高铁客运专线防灾系统的历史风速数据,建立了一种基于LSTM神经网络的大风预测模型,使用TensorFlow平台进行模型参数训练,并结合实际监测数据进行了模型验证。结果表明,该方法预测未来20 min的大风效果较好,预测20～30 m/s大风时的平均误差为13.4%。该研究可为高铁大风预警技术的应用提供参考。

High-speed railway gale prediction model and algorithm based on LSTM

Through the time series analysis of gale speed data,this paper established gale speed prediction model to implement the early warning of gale disaster,which was of great significance to improve the operation safety supportability of high-speed railway.The paper analyzed the historical gale speed data of the disaster prevention system of a high-speed railway passenger dedicated line,established a gale prediction model based on LSTM neural network,used TensorFlow platform to train the model parameters,and verified the model with the actual monitoring data.The results show that this method has a good effect in predicting the gale in the next 20 minutes,and the average error of 20~30 m/s gale prediction is 13.4%.The research can provide reference for the application of high-speed railway gale early warning technology.