ECCO Scientific, LLC, St. Petersburg, Florida, USA.
The Everglades Foundation, Palmetto Bay, Florida, USA.
Harmful Algae. 2024 Nov;139:102729. doi: 10.1016/j.hal.2024.102729. Epub 2024 Sep 24.
Karenia brevis blooms occur nearly annually along the southwest coast of Florida, and effective mitigation of ecological, public health, and economic impacts requires reliable real-time forecasting. We present two boosted random forest models that predict the weekly maximum K. brevis abundance category across the Greater Charlotte Harbor estuaries over one-week and four-week forecast horizons. The feature set was restricted to data available in near-real time, consistent with adoption of the models as decision-support tools. Features include current and lagged K. brevis abundance statistics, Loop Current position, sea surface temperature, sea level, and riverine discharges and nitrogen concentrations. During cross-validation, the one-week and four-week forecasts exhibited 73 % and 84 % accuracy, respectively, during the 2010-2023 study period. In addition, we assessed the models' reliability in forecasting the onset of 10 bloom events on time or in advance; the one-week and four-week models anticipated the onset eight times and five times, respectively.
短凯伦藻（Karenia brevis）几乎每年都会在佛罗里达州西南海岸爆发，为了减轻其对生态、公共健康和经济的影响，需要进行可靠的实时预测。我们提出了两种增强随机森林模型，可预测大夏洛特港河口地区未来一周和四周的每周最大短凯伦藻丰度类别。特征集限制在近实时可用的数据中，这与将模型作为决策支持工具的采用一致。特征包括当前和滞后的短凯伦藻丰度统计数据、回旋流位置、海面温度、海平面以及河流排放和氮浓度。在交叉验证期间，这两个模型在 2010 年至 2023 年的研究期间，分别在一周和四周的预测中达到了 73%和 84%的准确率。此外，我们评估了模型在及时或提前预测 10 次藻华事件开始方面的可靠性；一周和四周的模型分别有 8 次和 5 次提前预测到了藻华事件的开始。
