TY  - JOUR
PY  - 2024//
TI  - Typhoon disaster emergency forecasting method based on big data
JO  - PLoS one
A1  - Huo, Hong
A1  - Chen, Yuqiu
A1  - Wang, Shiying
SP  - e0299530
EP  - e0299530
VL  - 19
IS  - 4
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1932-6203
UR  - http://dx.doi.org/10.1371/journal.pone.0299530
ID  - ref1
ER  -