Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva
Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva
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DOI：
10.1016/j.hal.2020.101906
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发表时间：
2020-11-01
期刊：
影响因子：
6.6
通讯作者：
Jacquet, Stephan
中科院分区：
文献类型：
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作者：
Derot, Jonathan;Yajima, Hiroshi;Jacquet, Stephan
20世纪人类活动的发展增加了淡水生态系统中的营养物质通量，导致了富营养化现象，这种现象最常促进有害藻华(HABS)的发生。近年来，日内瓦湖中的一些丝状藻类或蓝藻有规律地大规模发展。因此，重要的水华可能对经济问题和人类健康造成有害影响。在这项研究中，我们试图为HAB预测模型奠定基础，以帮助科学家和当地利益相关者对这个高山周围湖泊的现在和未来进行管理。我们的预测策略是基于将两个机器学习模型与过去34年建立的长期数据库配对。我们通过K-Means模型创建了HAB组。然后，我们使用滑动窗口在随机森林(RF)模型的输入中引入了不同的滞后时间。最后，我们使用一个高频数据集，使用单个条件期望图比较了自然机制和数值交互作用。我们证明了一些赤潮事件可以在一年的尺度上进行预测。蓝藻浓度数据中包含的信息以四个强度组的形式合成，这四个强度组直接取决于冬凌草的浓度。这些数据的分类转换使我们能够获得相关系数保持在阈值0.5以上的预测，直到计数细胞一年和生物量数据两年。此外，我们还发现，RF模型预测了水温在14℃左右时冬凌草的最佳丰度，这一结果与有毒氰化细菌的生物学过程是一致的。在这项研究中，我们发现K-Means和RF模式之间的耦合可以帮助预测日内瓦湖形成水华的冬凌草的发展。这种方法可以创建一个数字决策支持工具，这对湖泊管理者来说应该是一个重大的优势。
The development of anthropic activities during the 20th century increased the nutrient fluxes in freshwater ecosystems, leading to the eutrophication phenomenon that most often promotes harmful algal blooms (HABs). Recent years have witnessed the regular and massive development of some filamentous algae or cyanobacteria in Lake Geneva. Consequently, important blooms could result in detrimental impacts on economic issues and human health. In this study, we tried to lay the foundation of an HAB forecast model to help scientists and local stakeholders with the present and future management of this peri-alpine lake. Our forecast strategy was based on pairing two machine learning models with a long-term database built over the past 34 years. We created HAB groups via a K-means model. Then, we introduced different lag times in the input of a random forest (RF) model, using a sliding window. Finally, we used a high-frequency dataset to compare the natural mechanisms with numerical interaction using individual conditional expectation plots.We demonstrate that some HAB events can be forecasted over a year scale. The information contained in the concentration data of the cyanobacteria was synthesized in the form of four intensity groups that directly depend on the P. rubescens concentration. The categorical transformation of these data allowed us to obtain a forecast with correlation coefficients that stayed above a threshold of 0.5 until one year for the counting cells and two years for the biovolume data. Moreover, we found that the RF model predicted the best P. rubescens abundance for water temperatures around 14 degrees C. This result is consistent with the biological processes of the toxic cyano-bacterium. In this study, we found that the coupling between K-means and RF models could help in forecasting the development of the bloom-forming P. rubescens in Lake Geneva. This methodology could create a numerical decision support tool, which should be a significant advantage for lake managers.