| 基于长短时记忆神经网络的台风路径临近预报模型 |
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| 引用本文： | 高松,赵鹏,潘斌,李亚汝,周敏,徐江玲,钟山,史振威.基于长短时记忆神经网络的台风路径临近预报模型[J].海洋学报(英文版),2018,37(5):8-12. |
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| 作者姓名： | 高松 赵鹏 潘斌 李亚汝 周敏 徐江玲 钟山 史振威 |
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| 作者单位： | 国家海洋局北海预报中心, 青岛, 266061;山东省海洋生态环境与防灾减灾重点实验室, 青岛, 266061,国家海洋局北海预报中心, 青岛, 266061;山东省海洋生态环境与防灾减灾重点实验室, 青岛, 266061,北京航空航天大学宇航学院图像处理中心, 北京, 100191,国家海洋局北海预报中心, 青岛, 266061;山东省海洋生态环境与防灾减灾重点实验室, 青岛, 266061,北京航空航天大学宇航学院图像处理中心, 北京, 100191,国家海洋局北海预报中心, 青岛, 266061;山东省海洋生态环境与防灾减灾重点实验室, 青岛, 266061,国家海洋局北海预报中心, 青岛, 266061;山东省海洋生态环境与防灾减灾重点实验室, 青岛, 266061,北京航空航天大学宇航学院图像处理中心, 北京, 100191 |
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| 基金项目： | The National Natural Science Foundation of China under contract Nos 61273245 and 41306028; the Beijing Natural Science Foundation under contract No. 4152031; the National Special Research Fund for Non-Profit Marine Sector under contract Nos 201405022-3 and 2013418026-4; the Ocean Science and Technology Program of North China Sea Branch of State Oceanic Administration under contract No. 2017A01; the Operational Marine Forecasting Program of State Oceanic Administration. |
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| 摘 要： | 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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| 关 键 词： | 台风路径 机器学习 长短时记忆神经网络 大数据 |
| 收稿时间： | 2016/7/16 0:00:00 |
| 修稿时间： | 2017/8/16 0:00:00 |
A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network |
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| GAO Song,ZHAO Peng,PAN Bin,LI Yaru,ZHOU Min,XU Jiangling,ZHONG Shan and 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. |
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| Authors: | GAO Song ZHAO Peng PAN Bin LI Yaru ZHOU Min XU Jiangling ZHONG Shan and SHI Zhenwei |
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| Institution: | 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, China2.Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China |
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| 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. |
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| Keywords: | typhoon tracks machine learning LSTM big data |
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