发表机构
University of Washington; NOAA Geophysical Fluid Dynamics Laboratory; Massachusetts Institute of Technology; University of California Davis（华盛顿大学; 美国国家海洋和大气管理局地球物理流体动力学实验室; 麻省理工学院; 加州大学戴维斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用无监督机器学习识别全球海洋生态省，并开发可解释的密集集成网络，基于卫星水色数据预测生态省，发现其可高技能推断，但输入与预测保真度存在权衡，强调不确定性量化与验证的重要性。
AI 中文摘要
海洋生态系统日益受到气候变化的影响，因此需要开发工具来识别和预测空间栖息地信息。为了构建此类工具，可以利用海洋生态省（即“生态省”）——全球海洋中具有生态意义的区域。我们使用无监督机器学习（ML）基于浮游植物功能类型的全球模拟输出，识别生态省及其相应的不确定性度量。我们的工作旨在创建一个基于卫星海洋水色数据预测生态省的概念验证。为此，我们开发了一个可解释的密集集成网络层级，以推断从模拟的海洋水色场中检测生态省的能力。关键结果表明，划分出的生态省既具有生态意义，又能以高技能被推断出来。然而，并未发现增加网络输入数据能持续提高推断技能的简单关系，输入与预测保真度之间存在复杂的权衡。我们的工作既是乐观的案例，也是需要不确定性量化和仔细验证预测保真度的警示故事。
英文摘要
Marine ecosystems are increasingly impacted by climate change, necessitating tools to identify and predict spatial habitat information. To build such tools, ecological marine provinces, "eco-provinces", ecologically meaningful regions in the global ocean can be used. We use unsupervised machine learning (ML) to identify eco-provinces with corresponding uncertainty measures based on output of a global simulation of phytoplankton functional types. Our work aims to create a proof of concept to predict eco-provinces based on satellite ocean color data. To do so, we develop a hierarchy of explainable dense ensemble networks to infer how well the eco-provinces can be detected from modeled ocean color fields. Key results include that the delineated eco-provinces are both ecologically meaningful and can be inferred with high skill. However, no straightforward relationship was found where adding more input data to the network consistently improves inference skill, and there is an intricate tradeoff between inputs and prediction fidelity. Our work is a case for optimism and a cautionary tale of needing uncertainty quantification and careful validation of prediction fidelity.
Comments23 pages 10 figures (15 without figures)
