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Typhoon disaster emergency forecasting method based on big data

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  • Hong Huo
  • Yuqiu Chen
  • Shiying Wang

Abstract

Typhoons are natural disasters characterized by their high frequency of occurrence and significant impact, often leading to secondary disasters. In this study, we propose a prediction model for the trend of typhoon disasters. Utilizing neural networks, we calculate the forgetting gate, update gate, and output gate to forecast typhoon intensity, position, and disaster trends. By employing the concept of big data, we collected typhoon data using Python technology and verified the model’s performance. Overall, the model exhibited a good fit, particularly for strong tropical storms. However, improvements are needed to enhance the forecasting accuracy for tropical depressions, typhoons, and strong typhoons. The model demonstrated a small average error in predicting the latitude and longitude of the typhoon’s center position, and the predicted path closely aligned with the actual trajectory.

Suggested Citation

  • Hong Huo & Yuqiu Chen & Shiying Wang, 2024. "Typhoon disaster emergency forecasting method based on big data," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-23, April.
    • Handle: RePEc:plo:pone00:0299530
    DOI: 10.1371/journal.pone.0299530
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    References listed on IDEAS

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    1. Liu, Na & Chen, Wenchuan & Wang, Jianyong & Shi, Huaizhi, 2023. "Typhoon strikes, distracted analyst and forecast accuracy: Evidence from China," Finance Research Letters, Elsevier, vol. 51(C).
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