Temporal Convolutional Network with Attention Mechanisms for Strong Wind Early Warning in High-Speed Railway Systems

Wei Gu, Guoyuan Yang (), Hongyan Xing, Yajing Shi and Tongyuan Liu
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Wei Gu: Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
Guoyuan Yang: Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
Hongyan Xing: School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yajing Shi: Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
Tongyuan Liu: Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China

Sustainability, 2025, vol. 17, issue 14, 1-20

Abstract: High-speed railway (HSR) is a key transport mode for achieving carbon reduction targets and promoting sustainable regional economic development due to its fast, efficient, and low-carbon nature. Accurate wind speed forecasting (WSF) is vital for HSR systems, as it provides future wind conditions that are critical for ensuring safe train operations. Numerous WSF schemes based on deep learning have been proposed. However, accurately forecasting strong wind events remains challenging due to the complex and dynamic nature of wind. In this study, we propose a novel hybrid network architecture, MHSETCN-LSTM, for forecasting strong wind. The MHSETCN-LSTM integrates temporal convolutional networks (TCNs) and long short-term memory networks (LSTMs) to capture both short-term fluctuations and long-term trends in wind behavior. The multi-head squeeze-and-excitation (MHSE) attention mechanism dynamically recalibrates the importance of different aspects of the input sequence, allowing the model to focus on critical time steps, particularly when abrupt wind events occur. In addition to wind speed, we introduce wind direction (WD) to characterize wind behavior due to its impact on the aerodynamic forces acting on trains. To maintain the periodicity of WD, we employ a triangular transform to predict the sine and cosine values of WD, improving the reliability of predictions. Massive experiments are conducted to evaluate the effectiveness of the proposed method based on real-world wind data collected from sensors along the Beijing–Baotou railway. Experimental results demonstrated that our model outperforms state-of-the-art solutions for WSF, achieving a mean-squared error (MSE) of 0.0393, a root-mean-squared error (RMSE) of 0.1982, and a coefficient of determination ( R 2 ) of 99.59%. These experimental results validate the efficacy of our proposed model in enhancing the resilience and sustainability of railway infrastructure.Furthermore, the model can be utilized in other wind-sensitive sectors, such as highways, ports, and offshore wind operations. This will further promote the achievement of Sustainable Development Goal 9.

Keywords: sustainable transportation; sustainable development; strong wind; high-speed railway system; time series (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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