Xiao Song, Jian-Feng Liang, Fang-Fang W A N, Xuan Y U, Xiaoxiao Shi, Lu-Yao H A N, Guang-Hao W E I, Bing Z H E N G, Akhir MohdFadzilMohd, Muslim Shawal M, Idris Izwandy
National Marine Data and Information Service, Tianjin, 300171, China.
National Marine Data and Information Service, Tianjin, 300171, China.
Mar Environ Res. 2022 Dec;182:105727. doi: 10.1016/j.marenvres.2022.105727. Epub 2022 Sep 11.
Red tide caused severe impacts on marine fisheries, ecology, economy and human life safety. The formation mechanism of the red tide is rather complicated; thus, red tide prediction and forecasting have long been a research hotspot around the globe. This study collected ocean monitoring data before and after the occurrence of red tides in Xiamen sea area from 2009 to 2017. The Pearson correlation coefficient method was used to obtain the associated factors of red tide occurrence, including water temperature, saturated dissolved oxygen, dissolved oxygen, chlorophyll-aand potential of hydrogen. Then, we built a short-time red tide prediction model based on the combination of multiple feature factors. chlorophyll-a, dissolved oxygen, saturated dissolved oxygen, potential of hydrogen, water temperature, salinity, turbidity, wind speed, wind direction and Air pressure were used as the input variables, building a short-time prediction model based on the combination of multiple feature factors to forecast red tide in the next 6 h by using the monitoring data. The accuracy of different forecast models with different feature combinations was compared. Results show that the distinguishing factors which have the most significant influence on red tide prediction in Xiamen are chlorophyll-a, dissolved oxygen, saturated dissolved oxygen, potential of hydrogen, and water temperature. the convergence speed of the Gated Recurrence Unit (GRU) prediction model based on the main feature factor proposed in this paper was faster and obtained the expected result, and the accuracy rates of the buoys are above 92%. The research shows the feasibility to use GRU network model to predict the occurrence of red tide with multi-feature factors as input parameters. the paper provides an effective method for the red tide early warning in Xiamen sea area.
赤潮对海洋渔业、生态、经济和人类生命安全造成了严重影响。赤潮的形成机制相当复杂，因此，赤潮预测长期以来一直是全球的研究热点。本研究收集了2009年至2017年厦门海域赤潮发生前后的海洋监测数据。采用Pearson相关系数法获取赤潮发生的相关因素，包括水温、饱和溶解氧、溶解氧、叶绿素a和酸碱度。然后，基于多个特征因子的组合建立了短期赤潮预测模型。将叶绿素a、溶解氧、饱和溶解氧、酸碱度、水温、盐度、浊度、风速、风向和气压作为输入变量，基于多个特征因子的组合建立短期预测模型，利用监测数据预测未来6小时的赤潮。比较了不同特征组合的不同预测模型的准确性。结果表明，对厦门赤潮预测影响最显著的判别因子是叶绿素a、溶解氧、饱和溶解氧、酸碱度和水温。本文提出的基于主要特征因子的门控循环单元（GRU）预测模型收敛速度更快，取得了预期效果，浮标的准确率均在92%以上。研究表明，以多特征因子为输入参数，利用GRU网络模型预测赤潮发生具有可行性。本文为厦门海域赤潮预警提供了一种有效方法。
