Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, Virginia.
Electrical and Computer Engineering and CS the Clarkson Center for Complex Systems Science, Clarkson University, Clarkson, New York USA.
Harmful Algae. 2022 Jan;111:102149. doi: 10.1016/j.hal.2021.102149. Epub 2021 Dec 12.
An accurate forecast of the red tide respiratory irritation level would improve the lives of many people living in areas affected by algal blooms. Using a decades-long database of daily beach conditions, two conceptually different models to forecast the respiratory irritation risk level one day ahead of time are trained. One model is wind-based, using the current days' respiratory level and the predicted wind direction of the following day. The other model is a probabilistic self-exciting Hawkes process model. Both models are trained on beaches in Florida during 2011--2017 and applied to the red tide bloom during 2018-2019. For beaches where there is enough historical data to develop a model, the model which performs best depends on the beach. The wind-based model is the most accurate at half the beaches, correctly predicting the respiratory risk level on average about 84% of the time. The Hawkes model is the most accurate (81% accuracy) at nearly all of the remaining beaches.
准确预测赤潮呼吸刺激水平将改善许多生活在受藻类大量繁殖影响地区的人们的生活。利用长达数十年的每日海滩状况数据库，训练了两个概念上不同的模型，以提前一天预测呼吸刺激风险水平。一个模型是基于风的，使用当天的呼吸水平和第二天的预测风向。另一个模型是概率自激发 Hawkes 过程模型。这两个模型都是在 2011 年至 2017 年期间在佛罗里达州的海滩上进行训练，并应用于 2018 年至 2019 年的赤潮爆发。对于有足够历史数据来开发模型的海滩，表现最好的模型取决于海滩。基于风的模型在一半的海滩上最准确，平均约 84%的时间正确预测呼吸风险水平。Hawkes 模型在几乎所有其余的海滩上的准确性最高（81%）。
