Citation

Huo H, Chen Y, Wang S. PLoS One 2024; 19(4): e0299530.

Copyright

(Copyright © 2024, Public Library of Science)

DOI

10.1371/journal.pone.0299530

PMID

38662787

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.

Language: en

Keywords

*Big Data; *Cyclonic Storms; *Forecasting/methods; Disaster Planning/methods; Disasters; Humans; Neural Networks, Computer