Bidirectional Spatio-Temporal Association Between the Observed Results of Ulva Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo
文献类型：期刊论文
| 作者 | Wang, Zhongyuan1; Fang, Zhixiang1; Zhang, Yu2; Song, Zhanlong3 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2021 |
| 卷号 | 14页码:5988-6008 |
| 关键词 | Green tide remote sensing (RS) social media spatial-temporal association Ulva prolifera |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2021.3085090 |
| 通讯作者 | Fang, Zhixiang(zxfang@whu.edu.cn) |
| 英文摘要 | Massive green tides caused by Ulva prolifera have annually occurred in the Yellow Sea since 2007, which has attracted much attention from the government and society. There are associations between the green tides in the Yellow Sea and social response in the social media (i.e., Sina Weibo), which are bidirectional and could be captured by the bidirectional neural network. For instance, how to detect daily U. prolifera green tides by fusing remote sensing data with social media data, and howto use the observed U. prolifera green tides to infer the social response are two challenges of StateOceanicAdministration, China. This article first illustrated that there are bidirectional associations between green tides and Sina Weibo data. Then, this article introduced a bidirectional spatio-temporal associative memory neural network (BSAMNN) model for modeling this bidirectional association from the spatio-temporal perspective. BSAMNN first extracted six characteristics fromgreen tides and nine characteristics fromthe social responses in 2016-2019. Second, these characteristics were split by year, and the characteristics in 2016-2018 were, respectively, put into the bidirectional associative memory neural network (BAM), which is a two-layer artificial neural network. Based on the BAM results and the observed data, the residual network was constructed. Third, BSAMNN extracted the spatio-temporal rules from the characteristics in 2016-2018 as the constraints through the mining rule algorithm and modify the results via sea surface wind and ocean surface current. Last, BSAMNN put the characteristics in 2019 into BAMand used the residual network to modify the results, which was constrained by the spatio-temporal rules. The feasibility and reliability of our approach were demonstrated by using the U. prolifera green tides in 2019. The average accuracy, false alarm rate, and missing alarm rate of BSAMNN results were 0.69, 0.25, and 0.31, respectively, which was 0.11 higher, 0.10 lower, and 0.11 lower than that of the traditional BAM. The results indicated that our method is an effective alternative of linking the U. prolifera green tides and its public sentiments on social media. |
| WOS关键词 | REMOTE-SENSING DATA ; EARTHQUAKE ; REGION ; EXTRACTION ; EXPANSION ; PATTERNS ; SOUTHERN ; IMPACT ; BLOOMS ; IMAGES |
| 资助项目 | National key Research and Development Plan[2017YFC1405302] ; National Natural Science Foundation of China[41771473] ; National Natural Science Foundation of China[41231171] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000670554200001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National key Research and Development Plan ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/163833]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Fang, Zhixiang |
| 作者单位 | 1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Beijing Global Safety Technol Co Ltd, Beijing 100010, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Zhongyuan,Fang, Zhixiang,Zhang, Yu,et al. Bidirectional Spatio-Temporal Association Between the Observed Results of Ulva Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:5988-6008. |
| APA | Wang, Zhongyuan,Fang, Zhixiang,Zhang, Yu,&Song, Zhanlong.(2021).Bidirectional Spatio-Temporal Association Between the Observed Results of Ulva Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,5988-6008. |
| MLA | Wang, Zhongyuan,et al."Bidirectional Spatio-Temporal Association Between the Observed Results of Ulva Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):5988-6008. |
入库方式： OAI收割
来源：地理科学与资源研究所
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