Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
School of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, China.
Mar Pollut Bull. 2020 Dec;161(Pt B):111731. doi: 10.1016/j.marpolbul.2020.111731. Epub 2020 Oct 30.
In eutrophic coastal waters, harmful algal blooms (HAB) often occur and present challenges to environmental and fisheries management. Despite decades of research on HAB early warning systems, the field validation of algal bloom forecast models have received scant attention. We propose a daily algal bloom risk forecast system based on: (i) a vertical stability theory verified against 191 past algal bloom events; and (ii) a data-driven artificial neural network (ANN) model that assimilates high frequency data to predict sea surface temperature (SST), vertical temperature and salinity differential with an accuracy of 0.35C, 0.51C, and 0.58 psu respectively. The model does not rely on past chlorophyll measurements and has been validated against extensive field data. Operational forecasts are illustrated for representative algal bloom events at a marine fish farm in Tolo Harbour, Hong Kong. The robust model can assist with traditional onsite monitoring as well as artificial-intelligence (AI) based methods.
在富营养化的沿海水域，有害藻类大量繁殖（HAB）经常发生，给环境和渔业管理带来挑战。尽管对有害藻类大量繁殖预警系统进行了数十年的研究，但藻类大量繁殖预测模型的现场验证却很少受到关注。我们提出了一种基于以下内容的每日藻类大量繁殖风险预测系统：（i）经过 191 次过去藻类大量繁殖事件验证的垂直稳定性理论；和（ii）一种数据驱动的人工神经网络（ANN）模型，该模型可以同化高频数据，以预测海面温度（SST）、垂直温度和盐度差，准确度分别为 0.35°C、0.51°C 和 0.58 psu。该模型不依赖于过去的叶绿素测量值，并已通过广泛的现场数据进行了验证。针对香港吐露港一个海洋鱼类养殖场的代表性藻类大量繁殖事件进行了操作预测。稳健的模型可以协助传统的现场监测以及基于人工智能（AI）的方法。
