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

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