Wind Forecasting in Railway Engineering presents core and leading-edge technologies in wind forecasting for railway engineering. The title brings together wind speed forecasting and railway wind engineering, offering solutions from both fields. Key technologies are presented, along with theories, modeling steps and comparative analyses of forecasting technologies. Each chapter presents case studies and applications, including typical applications and key issues, analysis of wind field characteristics, optimization methods for the placement of a wind anemometer, single-point time series along railways, deep learning algorithms on single-point wind forecasting, reinforcement learning algorithms, ensemble single-point wind forecasting methods, spatial wind, and data-driven spatial-temporal wind forecasting algorithms.

This important book offers practical solutions for railway safety, by bringing together the latest technologies in wind speed forecasting and railway wind engineering into a single volume.

• Presents the core technologies and most advanced developments in wind forecasting for railway engineering
• Gives case studies and experimental designs, demonstrating real-world applications
• Introduces cutting-edge deep learning and reinforcement learning methods
• Combines the latest thinking from wind engineering and railway engineering
• Offers a complete solution to wind forecasting in railway engineering for the safety of running trains

Wind Forecasting in Railway Engineering presents core and leading-edge technologies in wind forecasting for railway engineering. The title brings together wind speed forecasting and railway wind engineering, offering solutions from both fields. Key technologies are presented, along with theories, modeling steps and comparative analyses of forecasting technologies. Each chapter presents case studies and applications, including typical applications and key issues, analysis of wind field characteristics, optimization methods for the placement of a wind anemometer, single-point time series along railways, deep learning algorithms on single-point wind forecasting, reinforcement learning algorithms, ensemble single-point wind forecasting methods, spatial wind, and data-driven spatial-temporal wind forecasting algorithms.

This important book offers practical solutions for railway safety, by bringing together the latest technologies in wind speed forecasting and railway wind engineering into a single volume.

• Presents the core technologies and most advanced developments in wind forecasting for railway engineering
• Gives case studies and experimental designs, demonstrating real-world applications
• Introduces cutting-edge deep learning and reinforcement learning methods
• Combines the latest thinking from wind engineering and railway engineering
• Offers a complete solution to wind forecasting in railway engineering for the safety of running trains

Wind Forecasting in Railway Engineering presents core and leading-edge technologies in wind forecasting for railway engineering. The title brings together wind speed forecasting and railway wind engineering, offering solutions from both fields. Key technologies are presented, along with theories, modeling steps and comparative analyses of forecasting technologies. Each chapter presents case studies and applications, including typical applications and key issues, analysis of wind field characteristics, optimization methods for the placement of a wind anemometer, single-point time series along railways, deep learning algorithms on single-point wind forecasting, reinforcement learning algorithms, ensemble single-point wind forecasting methods, spatial wind, and data-driven spatial-temporal wind forecasting algorithms.

This important book offers practical solutions for railway safety, by bringing together the latest technologies in wind speed forecasting and railway wind engineering into a single volume.

• Presents the core technologies and most advanced developments in wind forecasting for railway engineering
• Gives case studies and experimental designs, demonstrating real-world applications
• Introduces cutting-edge deep learning and reinforcement learning methods
• Combines the latest thinking from wind engineering and railway engineering
• Offers a complete solution to wind forecasting in railway engineering for the safety of running trains