| 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 |
| 基于长短时记忆神经网络的台风路径临近预报模型 |
| A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network |
| 投稿时间：2016-07-16 修订日期：2017-08-16 |
| DOI：10.1007/s13131-018-1219-z |
| 中文关键词: 台风路径 机器学习 长短时记忆神经网络 大数据 |
| 英文关键词:typhoon tracks machine learning LSTM big data |
| 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. |
| 作者 | 单位 | E-mail | | 高松 | 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 | | | 赵鹏 | 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 | | | 潘斌 | 北京航空航天大学宇航学院图像处理中心, 北京, 100191 | | | 李亚汝 | 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 | liyaru@bhfj.gov.cn | | 周敏 | 北京航空航天大学宇航学院图像处理中心, 北京, 100191 | | | 徐江玲 | 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 | | | 钟山 | 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 | | | 史振威 | 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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| 中文摘要: |
| 对台风路径进行准确预报能够有效降低人员与经济损失。长期以来我国气象部门观测到海量台风数据，然而，当前这些数据尚未得到充分的利用。基于机器学习算法的预测技术是一种有效的数据分析手段。利用1949-2011年间全部登陆中国大陆的台风数据，结合大数据与数据挖掘技术，训练一种长短时记忆神经网络（Long Short Term Memory，LSTM）模型，构建基于机器学习算法的台风路径预测模型。实验结果表明，本文算法能够提供6-24h内相对准确的台风路径临近预报，提高台风路径的预报精度。 |
| 英文摘要: |
| 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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