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

尽管人类活动对湖泊的影响越来越大，但在大陆尺度上关于湖泊水质的信息却非常稀少。此外，我们对气候和土地利用等关键驱动因素的大规模变化如何控制大陆尺度上的水质只有初步的了解。因此，理解基于少数几个研究相对充分的湖泊的生态知识如何应用于美国大陆是一个挑战，因为美国大陆的数据有限，只有数千个湖泊。由于湖泊数量众多以及水质问题的复杂性，机器学习可能会被证明是有用的。然而，最近在商业应用中取得巨大成功的机器学习进展尚未完全应用于自然系统中的问题，如湖泊水质，部分原因是数据量较低。此外，基础生态学研究的一个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所缺少的目标。该项目开发了生态知识引导机器学习（Eco-KGML），作为一个框架，利用生态理解和机器学习在美国湖泊水质建模中的力量。Eco-KGML提高了水质预测的准确性，并推进了有关水质过程的新知识的发现。为了扩大这项工作的影响，该项目通过一个由本科生组成的培训计划，支持妇女和代表性不足的少数民族参与STEM（科学、技术、工程和数学），这些学生从历史上被排斥的群体中招募，每年夏天参加Eco-KGML研究项目。该计划提供真实的研究经验，在学年期间演变为个人研究项目，并在一个支持性的环境中让学生参与跨学科，跨机构，合作的科学。该项目还通过制作和传播一个向学生介绍生态kgml概念的互动软件模块来改善STEM教育。通过与美国联邦机构合作伙伴和国家生态观测网络（NEON）的合作者的合作，该项目的更广泛影响超出了参与大学的范围，这些合作伙伴为机构和NEON的优先事项提供信息和反馈。该项目开发了生态知识引导机器学习（Eco-KGML）作为宏观系统尺度上湖泊和水库水质（WQ）动态建模的概念框架。Eco-KGML使用基于动态过程的模型和ML模型的混合组合，在地理上广泛的WQ数据的帮助下，将WQ过程从经过充分研究的湖泊扩展到美国的宏观系统尺度。本项目主要关注水体清晰度、浮游植物生物量和低代谢缺氧的具体WQ指标，以解决以下问题：控制水质的主要过程是什么？它们如何随空间和时间变化？从局部到宏观系统尺度，气候、土地利用和生态系统记忆如何相互作用影响水质动态？湖泊水质变化的广泛空间和长期格局是什么？为了解决这些问题，在湖泊WQ的Eco-KGML模型中开启了一条新的研究路线，这不仅旨在提高WQ变量的预测性能，而且还可以在一定的时空尺度上发现关于WQ过程的新知识。利用机器学习方法，在给定湖泊WQ观测值的情况下，探索了一种计算效率高、可推广的湖泊过程参数估计新方法。基于ml的湖泊WQ模型能够发现每个湖泊的WQ变量之间的新关系，并提取相关的时间滞后。通过模块化组成学习（MCL）的新研究，Eco-KGML模型被开发出来，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动态。此外，Eco-KGML模型根据其生态特征学习和区分单个湖泊特有的过程和跨类型湖泊的过程。这种对科学知识和数据的灵活和全面利用，使研究湖泊及其驱动因素之间的尺度依赖关系成为可能，同时为跨多个时空尺度的湖泊提供更可靠的预测。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.