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This study introduces a neural network model to predict typhoon disaster trends, intensity, and paths using big data. The model shows promise, especially for strong tropical storms, with accurate path predictions.

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Area of Science:

  • Meteorology and Atmospheric Sciences
  • Data Science and Artificial Intelligence
  • Disaster Management

Background:

  • Typhoons are frequent natural disasters with substantial impacts, often triggering secondary disasters.
  • Accurate forecasting of typhoon intensity, position, and associated disaster trends is crucial for mitigation and preparedness.

Purpose of the Study:

  • To develop and evaluate a neural network-based prediction model for typhoon disaster trends.
  • To forecast typhoon intensity, position, and disaster trajectories using advanced computational techniques.

Main Methods:

  • Utilized neural networks, specifically calculating forgetting, update, and output gates for forecasting.
  • Employed big data concepts and Python technology for collecting and processing typhoon data.
  • Verified model performance through empirical analysis of collected typhoon datasets.

Main Results:

  • The model demonstrated a good fit, particularly for strong tropical storms.
  • Forecasting accuracy requires improvement for tropical depressions, typhoons, and strong typhoons.
  • The model achieved a small average error in predicting the typhoon's center latitude and longitude, with predicted paths closely matching actual trajectories.

Conclusions:

  • The proposed neural network model shows potential for predicting typhoon disaster trends and trajectories.
  • The model's accuracy is promising for intense typhoons but needs enhancement for weaker systems.
  • Further research is recommended to refine the model for improved forecasting across all typhoon categories.