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

Abstract

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 how to use the observed U. prolifera 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 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.

Keywords

• Green tide
• remote sensing (RS)
• social media
• spatial-temporal association
• Ulva prolifera%22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> <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