有害藻华影响管理中的混合机器学习技术
Hybrid Machine Learning techniques in the management of harmful algal blooms impact
AI总结：
针对有害藻华影响贝类养殖的问题，研究提出以生产区活动状态为目标变量，比较了BAGNET等混合机器学习模型，发现BAGNET在藻华检测中泛化能力与鲁棒性最优。
摘要：
有害藻华（HABs）是指藻类浓度过高且可能对人类食用具有潜在毒性的现象。贝类养殖可能会受到HABs的影响，因为贝类作为滤食性动物，会在其组织中积累高浓度的海洋生物毒素。为避免对人类食用的风险，一旦检测到毒性，便会禁止采收。目前，生产区的关闭是基于专家知识，而在条件复杂且无法进行采样时，一个预测模型将能提供帮助。尽管贝肉中的毒素浓度是专家在控制贝类生产区时最常用的方法，但它很少被自动预测模型用作预测目标。这在很大程度上是由于既定采样计划导致的数据不规则性。作为替代方案，生产区的活动状态已被提出作为目标变量，其依据是贝肉毒性水平低于或高于法定限值。这一新选项与贝类生产区控制的实际运作方式最为相似。为此，我们在估计生产区状态时，对 Neural-Network-Adding Bootstrap (BAGNET) 和 Discriminative Nearest Neighbor Classification (SVM-KNN) 等混合机器学习模型进行了比较。该研究在藻华事件复杂程度不同的多个河口进行，以证明模型在藻华检测中的泛化能力。结果表明，BAGNET 的平均召回率（recall）达到 93.41%，且在任何河口都不低于 90%，在结果和鲁棒性方面均优于其他模型。
英文摘要：
Harmful algal blooms (HABs) are episodes of high concentrations of algae that are potentially toxic for human consumption. Mollusc farming can be affected by HABs because, as filter feeders, they can accumulate high concentrations of marine biotoxins in their tissues. To avoid the risk to human consumption, harvesting is prohibited when toxicity is detected. At present, the closure of production areas is based on expert knowledge and the existence of a predictive model would help when conditions are complex and sampling is not possible. Although the concentration of toxin in meat is the method most commonly used by experts in the control of shellfish production areas, it is rarely used as a target by automatic prediction models. This is largely due to the irregularity of the data due to the established sampling programs. As an alternative, the activity status of production areas has been proposed as a target variable based on whether mollusc meat has a toxicity level below or above the legal limit. This new option is the most similar to the actual functioning of the control of shellfish production areas. For this purpose, we have made a comparison between hybrid machine learning models like Neural-Network-Adding Bootstrap (BAGNET) and Discriminative Nearest Neighbor Classification (SVM-KNN) when estimating the state of production areas. The study has been carried out in several estuaries with different levels of complexity in the episodes of algal blooms to demonstrate the generalization capacity of the models in bloom detection. As a result, we could observe that, with an average recall value of 93.41% and without dropping below 90% in any of the estuaries, BAGNET outperforms the other models both in terms of results and robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
