Journal
ACTA OCEANOLOGICA SINICA
Volume 37, Issue 5, Pages 8-12Publisher
SPRINGER
DOI: 10.1007/s13131-018-1219-z
Keywords
typhoon tracks; machine learning; LSTM; big data
Categories
Funding
- National Natural Science Foundation of China [61273245, 41306028]
- Beijing Natural Science Foundation [4152031]
- National Special Research Fund for Non-Profit Marine Sector [201405022-3, 2013418026-4]
- Ocean Science and Technology Program of North China Sea Branch of State Oceanic Administration [2017A01]
- Operational Marine Forecasting Program of State Program of State Oceanic Administration
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It is of vital importance to reduce injuries and economic losses by accurate forecasts of typhoon tracks. A huge amount of typhoon observations have been accumulated by the meteorological department, however, they are yet to be adequately utilized. It is an effective method to employ machine learning to perform forecasts. A long short term memory (LSTM) neural network is trained based on the typhoon observations during 1949-2011 in China's Mainland, combined with big data and data mining technologies, and a forecast model based on machine learning for the prediction of typhoon tracks is developed. The results show that the employed algorithm produces desirable 6-24 h nowcasting of typhoon tracks with an improved precision.
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