An early forecasting method for the drift path of green tides: A case study in the Yellow Sea, China
文献类型：期刊论文
| 作者 | Hu, Po1,2 ; Liu, Yahao1,2; Hou, Yijun1,2,3; Yi, Yuqi1,2
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| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
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| 出版日期 | 2018-09-01 |
| 卷号 | 71页码:121-131 |
| 关键词 | Green tides The Yellow Sea Drift path Artificial neural network Numerical model |
| ISSN号 | 0303-2434 |
| DOI | 10.1016/j.jag.2018.05.001 |
| 通讯作者 | Liu, Yahao(yhliu@qdio.ac.cn) |
| 英文摘要 | Since 2007, green tides caused by massive blooms of Enteromorpha prolifera have occurred in the Yellow Sea during April and September every year. Generally, the macroalgae first gathered around the Jiangsu coastline and then moved northeastward toward the Shandong Peninsula, but the paths and distribution of green tides have featured obvious inter-annual variation. Here, we describe a new method to forecasting the drift path of green tides with some climate indices such as Nino3.4. This method may help policy makers to develop a strategy to prevent green tide disasters and mitigate the consequence more effectively. Initially, we ran a numerical ocean model to simulate the movement of hypothetical green tides for last 20 years. The model was driven by remote sensing data of sea surface winds, surface temperatures, and tracers representing macroalgae that were created on certain dates so that drift paths could be traced. Ocean color remote sensing data were employed to determine the drift parameters. Next, the relationship between the displacement of tracers, including directions and distances of movement during certain periods were then analyzed along with the corresponding values of a set of six climate indices. A forecasting algorithm based on an artificial neural network was then established and trained with these data. Using this algorithm, the drift path of green tides could be predicted from the values of certain climate indices of the previous year. The model assessment with satellite ocean color remote sensing images indicated the effectiveness and practicability of this method. |
| 资助项目 | National Key Research and Development Program of China[2016YFC1402000] ; National Natural Science Foundation of China[41476018] ; National Natural Science Foundation of China[41421005] ; National Natural Science Foundation of China[U1406401] ; Public Science and Technology Research Funds Projects of the Ocean[201205010] ; CAS[XDA19060202] ; High-Performance Computing Center, IOCAS |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000441116900011 |
| 出版者 | ELSEVIER SCIENCE BV |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/159682]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Liu, Yahao |
| 作者单位 | 1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Qingdao Natl Lab Marine Sci & Technol, Qingdao 266237, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100029, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Po,Liu, Yahao,Hou, Yijun,et al. An early forecasting method for the drift path of green tides: A case study in the Yellow Sea, China[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2018,71:121-131. |
| APA | Hu, Po,Liu, Yahao,Hou, Yijun,&Yi, Yuqi.(2018).An early forecasting method for the drift path of green tides: A case study in the Yellow Sea, China.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,71,121-131. |
| MLA | Hu, Po,et al."An early forecasting method for the drift path of green tides: A case study in the Yellow Sea, China".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 71(2018):121-131. |
入库方式： OAI收割
来源：海洋研究所
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