A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network

GAO Song; ZHAO Peng; PAN Bin; LI Yaru; ZHOU Min; XU Jiangling; ZHONG Shan; SHI Zhenwei

doi:10.1007/s13131-018-1219-z

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Article Navigation > Acta Oceanologica Sinica > 2018 > 37(5): 8-12

GAO Song, ZHAO Peng, PAN Bin, LI Yaru, ZHOU Min, XU Jiangling, ZHONG Shan, SHI Zhenwei. A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network[J]. Acta Oceanologica Sinica, 2018, 37(5): 8-12. doi: 10.1007/s13131-018-1219-z

Citation:

GAO Song, ZHAO Peng, PAN Bin, LI Yaru, ZHOU Min, XU Jiangling, ZHONG Shan, SHI Zhenwei. A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network[J]. Acta Oceanologica Sinica, 2018, 37(5): 8-12. doi: 10.1007/s13131-018-1219-z

Citation:

GAO Song, ZHAO Peng, PAN Bin, LI Yaru, ZHOU Min, XU Jiangling, ZHONG Shan, SHI Zhenwei. A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network[J]. Acta Oceanologica Sinica, 2018, 37(5): 8-12. doi: 10.1007/s13131-018-1219-z

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A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network

doi: 10.1007/s13131-018-1219-z

1.
North China Sea Marine Forecasting Center of State Oceanic Administration, State Oceanic Administration, Qingdao 266061, China;Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China
2.
Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China

Received Date: 2016-07-16
Rev Recd Date: 2017-08-16

Abstract

Abstract

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.
- typhoon tracks,
- machine learning,
- LSTM,
- big data

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References(16)

References

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