Author(s)

Xue Wang ¹, Xueting Qin ¹

Affiliation(s)

¹ Faculty of Construction, Guangdong Technology College, Zhaoqing, Guangdong, China

Corresponding Author

Xueting Qin

ABSTRACT

Tropical cyclones (TCs) are among the most destructive natural disasters, posing significant threats to coastal regions, particularly in southeastern China, where rapid economic growth and urbanization have intensified the risks associated with TC-induced wind hazards. To enhance predictive capabilities and mitigate potential damage, this study leverages advanced artificial intelligence (AI) techniques, focusing on deep learning-based Variational Autoencoder (VAE) models, to analyze and forecast the genesis of tropical cyclones in the Northwest Pacific. By training the VAE on historical TC data, the model effectively captures the underlying patterns governing TC formation, enabling accurate simulations of both the frequency and spatial distribution of these events. The findings reveal that the VAE model performs robustly in replicating observed TC climatology, offering critical insights for risk assessment and disaster preparedness. Furthermore, the study highlights the practical applications of AI-driven TC predictions in civil engineering, particularly in improving wind load calculations and optimizing structural designs to enhance resilience against extreme wind events. This research underscores the potential of AI technologies in advancing meteorological forecasting and supporting sustainable infrastructure development in cyclone-prone regions.

KEYWORDS

Tropical Cyclone, Artificial Intelligence, Variational Autoencoder, Wind Load, Building Structure

CITE THIS PAPER

Xue Wang, Xueting Qin, Prediction and Analysis of Tropical Cyclone Genesis Based on Artificial Intelligence Technology and Its Application in Civil Engineering. Journal of Civil Engineering and Urban Planning (2025) Vol. 7: 71-77. DOI: http://dx.doi.org/10.23977/jceup.2025.070109.

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Sponsors, Associates, and Links

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• Journal of Sustainable Development and Green Buildings
  
  
• Landscape and Urban Horticulture
  
  
• Bridge and Structural Engineering
  
  
• Soil Mechanics and Geotechnical Engineering
  
  
• Journal of Municipal Engineering
  
  
• Heating, Ventilation and Air Conditioning
  
  
• Indoor Air Quality and Climate
  
  
• Computer Aided Architecture Design