Collaborative Research: MRA: Advancing process understanding of lake water quality to macrosystem scales with knowledge-guided machine learning

合作研究：MRA：通过知识引导的机器学习将湖泊水质的过程理解推进到宏观系统尺度

• 批准号: 2213549
• 负责人: Paul Hanson
• 金额: $ 52.59万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-01 至 2026-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213549&HistoricalAwards=false
• 关键词: Collaborative; Research; MRA; Advancing; process

Despite the growing influence of human activities on lakes, there is remarkably sparse information on lake water quality at continental scales. Moreover, we have only a nascent understanding of how broadscale changes in key drivers, such as climate and land use, control water quality at continental scales. Thus, it is a challenge to understand how ecological knowledge, based on a few relatively well-studied lakes, applies to the continental U.S., where data are limited for 1000s of lakes. Because of the large number of lakes and the complexity of the water quality problem, machine learning may prove useful. However, recent advances in machine learning that have shown great success in commercial applications have yet to be fully applied to problems in natural systems, such as lake water quality, in part because of lower data volumes. In addition, a fundamental goal of basic ecological research is mechanistic understanding of the way the world works, a goal missing in many machine learning approaches. This project develops ecology-knowledge guided machine learning (Eco-KGML) as a framework for leveraging the power of both ecological understanding and machine learning in modeling lake water quality across the U.S. Eco-KGML improves the accuracy of water quality predictions and advances the discovery of new knowledge about water quality processes. To broaden the impacts of this work, the project supports participation of women and underrepresented minorities in STEM (science, technology, engineering, and math) through a training program consisting of cohorts of undergraduate students, recruited from historically-excluded groups, who work on Eco-KGML research projects each summer. This program provides authentic research experiences that evolve into individual research projects during the academic year and engage students in cross-disciplinary, cross-institutional, collaborative science in a supportive environment. This project also improves STEM education through production and dissemination of an interactive software module that introduces students to Eco-KGML concepts. The broader impact of this project extends beyond the participating universities through collaborations with U.S. federal agency partners and collaborators from the National Ecological Observatory Network (NEON) that inform, and feed back to, agency and NEON priorities. This project develops ecology-knowledge guided machine learning (Eco-KGML) as a conceptual framework for modeling lake and reservoir water quality (WQ) dynamics at macrosystem scales. Eco-KGML uses hybrid combinations of dynamical process-based models and ML models to scale WQ processes from well-studied lakes to macrosystem-scales across the U.S with the help of geographically extensive WQ data. This project focuses on the specific WQ metrics of water clarity, phytoplankton biomass, and hypolimnetic anoxia, in addressing the questions: What are the dominant processes governing water quality and how do they vary across space and time? How do climate, land use, and ecosystem memory interact to affect water quality dynamics from local to macrosystem-scales? What are the broad spatial and long-term patterns of change in lake water quality? In addressing these questions, a new line of research is enabled in Eco-KGML models for lake WQ, which are not only aimed at improving predictive performance of WQ variables but can also lead to discovery of new knowledge about WQ processes at a range of spatio-temporal scales. Novel research in estimating process parameters of a lake, given its WQ observations, in a computationally efficient and generalizable manner is explored using ML methods. The ML-based models for lake WQ enable the discovery of new relationships among WQ variables at every lake, along with extracting relevant time lags. Through novel research in modular compositional learning (MCL), Eco-KGML models are developed to identify which WQ processes are dominant at a given lake and how they interact to influence overall WQ dynamics. Moreover, the Eco-KGML models learn and distinguish processes specific to a single lake from those that generalize across types of lakes according to its ecological characteristics. This flexible and comprehensive use of both scientific knowledge and data enable the study of scale-dependent relationships between lakes and their drivers while providing more robust predictions for lakes across multiple temporal and spatial scales.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

尽管人类活动对湖泊的影响不断增长，但在大陆尺度上，关于湖水质量的信息稀疏。此外，我们对主要驱动因素的广泛变化（例如气候和土地使用，在大陆尺度上控制水质控制水质）有一个新生的了解。因此，了解基于一些相对良好的湖泊的生态知识如何适用于美国大陆的生态知识，在美国大陆上，数据的数据受到1000湖的限制。由于湖泊的数量大量和水质问题的复杂性，机器学习可能很有用。但是，在商业应用中显示出巨大成功的机器学习进展尚未完全应用于湖水质量等自然系统中的问题，部分原因是数据量较低。此外，基本生态学研究的基本目标是对世界运作方式的机械理解，这是许多机器学习方法中缺少的目标。该项目开发了生态知识指导的机器学习（ECO-KGML），作为利用生态学理解和机器学习在模拟整个美国生态水质量方面的力量的框架，可提高水质预测的准确性，并提高新知识在水质过程中的发现。为了扩大这项工作的影响，该项目支持妇女和代表性不足的少数群体参与STEM（科学，技术，工程和数学），该培训计划由每年夏天从事历史悠久的群体招募的本科生组成的培训计划。该计划提供了真实的研究经验，这些研究经验在学年期间发展成为单个研究项目，并在支持环境中吸引学生跨学科，跨机构的协作科学。该项目还通过生产和传播交互式软件模块来改善STEM教育，该软件模块向学生介绍了生态 - 千兆数概念。通过与美国联邦机构合作伙伴和国家生态天文台网络（NEON）的合作者的合作，该项目的更广泛影响超出了参与大学，这些大学的合作将为代理和霓虹灯的优先事项提供信息。该项目开发了生态知识指导的机器学习（ECO-KGML），作为在宏观系统量表上建模湖泊和水库水质（WQ）动态的概念框架。 Eco-KGML使用基于动力学过程的模型和ML模型的混合组合来扩展WQ过程，并在地理位置广泛的WQ数据​​的帮助下，从研究良好的湖泊到美国的宏观系统大规模。该项目侧重于水清晰度，浮游植物生物量和迟发性缺氧的特定WQ指标，以解决问题：哪些主要过程来管理水质，它们如何在空间和时间上变化？气候，土地使用和生态系统记忆如何相互作用以影响从本地到宏观系统范围的水质动态？湖水质量变化的广泛空间和长期模式是什么？在解决这些问题时，在WQ湖的生态-KGML模型中实现了一系列新的研究，不仅旨在改善WQ变量的预测性能，而且还可以导致在一系列时空尺度上发现有关WQ过程的新知识。鉴于其WQ观测值，通过ML方法探索了以计算高效且可推广的方式估算湖泊的过程参数的新研究。基于ML的WQ湖模型可以在每个湖泊的WQ变量之间发现新的关系，并提取相关时间滞后。通过模块化组成学习（MCL）的新研究，开发了生态 - KGML模型，以确定哪些WQ过程在给定的湖泊中占主导地位，以及它们如何相互作用以影响整体WQ动力学。此外，生态 - 千贵族模型学习并区分单个湖泊的过程与那些根据其生态特征跨越类型的湖泊的过程。科学知识和数据的这种灵活而全面的使用使湖泊及其驾驶员之间的规模依赖关系的研究可以研究在多个时间和空间尺度上为湖泊提供更强大的预测。该奖项反映了NSF的法定任务，并通过评估该基金会的智力优点和广泛的影响来评估CRITERIA。