Bidirectional Spatio-Temporal Association Between the Observed Results of <italic>Ulva</italic> Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo
Massive green tides caused by <italic>Ulva</italic> 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...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9444580/ |
| _version_ | 1818621029446057984 |
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| author | Zhongyuan Wang Zhixiang Fang Yu Zhang Zhanlong Song |
| author_facet | Zhongyuan Wang Zhixiang Fang Yu Zhang Zhanlong Song |
| author_sort | Zhongyuan Wang |
| collection | DOAJ |
| description | Massive green tides caused by <italic>Ulva</italic> 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 <italic>U. prolifera</italic> green tides by fusing remote sensing data with social media data, and how to use the observed <italic>U. prolifera</italic> green tides to infer the social response are two challenges of State Oceanic Administration, 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 from green tides and nine characteristics from the 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 BAM and 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 <italic>U. prolifera</italic> 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 <italic>U. prolifera</italic> green tides and its public sentiments on social media. |
| first_indexed | 2024-12-16T18:02:47Z |
| format | Article |
| id | doaj.art-cf7f921921c540f2ba99acf8d2797900 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-16T18:02:47Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-cf7f921921c540f2ba99acf8d27979002022-12-21T22:22:01ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01145988600810.1109/JSTARS.2021.30850909444580Bidirectional Spatio-Temporal Association Between the Observed Results of <italic>Ulva</italic> Prolifera Green Tides in the Yellow Sea and the Social Response in Sina WeiboZhongyuan Wang0https://orcid.org/0000-0003-3268-6177Zhixiang Fang1https://orcid.org/0000-0003-1651-878XYu Zhang2Zhanlong Song3State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaBeijing Global Safety Technology Co., Ltd., Beijing, ChinaMassive green tides caused by <italic>Ulva</italic> 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 <italic>U. prolifera</italic> green tides by fusing remote sensing data with social media data, and how to use the observed <italic>U. prolifera</italic> green tides to infer the social response are two challenges of State Oceanic Administration, 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 from green tides and nine characteristics from the 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 BAM and 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 <italic>U. prolifera</italic> 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 <italic>U. prolifera</italic> green tides and its public sentiments on social media.https://ieeexplore.ieee.org/document/9444580/Green tideremote sensing (RS)social mediaspatial-temporal association<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Ulva</italic> prolifera |
| spellingShingle | Zhongyuan Wang Zhixiang Fang Yu Zhang Zhanlong Song Bidirectional Spatio-Temporal Association Between the Observed Results of <italic>Ulva</italic> 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 Green tide remote sensing (RS) social media spatial-temporal association <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Ulva</italic> prolifera |
| title | Bidirectional Spatio-Temporal Association Between the Observed Results of <italic>Ulva</italic> Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo |
| title_full | Bidirectional Spatio-Temporal Association Between the Observed Results of <italic>Ulva</italic> Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo |
| title_fullStr | Bidirectional Spatio-Temporal Association Between the Observed Results of <italic>Ulva</italic> Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo |
| title_full_unstemmed | Bidirectional Spatio-Temporal Association Between the Observed Results of <italic>Ulva</italic> Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo |
| title_short | Bidirectional Spatio-Temporal Association Between the Observed Results of <italic>Ulva</italic> Prolifera Green Tides in the Yellow Sea and the Social Response in Sina Weibo |
| title_sort | bidirectional spatio temporal association between the observed results of italic ulva italic prolifera green tides in the yellow sea and the social response in sina weibo |
| topic | Green tide remote sensing (RS) social media spatial-temporal association <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Ulva</italic> prolifera |
| url | https://ieeexplore.ieee.org/document/9444580/ |
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