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: 2213550
• 负责人: Anuj Karpatne
• 金额: $ 56.73万
• 依托单位: Virginia Polytechnic Institute and State University
• 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=2213550&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越来越大，但在大陆尺度上有关湖泊水质的信息非常稀少。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的水质只有初步的了解。因此，要了解基于几个相对研究得比较好的湖泊的生态知识如何适用于美国大陆是一个挑战，其中数据仅限于1000个湖泊。由于大量的湖泊和水质问题的复杂性，机器学习可能会被证明是有用的。然而，机器学习的最新进展在商业应用中取得了巨大成功，但尚未完全应用于自然系统中的问题，例如湖泊水质，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的机器学习（Eco-KGML）作为一个框架，利用生态理解和机器学习的力量来模拟美国各地的湖泊水质。Eco-KGML提高了水质预测的准确性，并促进了对水质过程新知识的发现。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民族参与STEM（科学，技术，工程和数学），通过一个培训计划，由一批本科生组成，从历史上被排斥的群体中招募，他们每年夏天从事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，并让学生在支持性环境中参与跨学科，跨机构，协作科学。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（氖）的合作者合作，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氖的优先事项提供信息和反馈。该项目开发了生态知识引导的机器学习（Eco-KGML），作为在宏观系统尺度上模拟湖泊和水库水质（WQ）动态的概念框架。Eco-KGML使用基于动态过程的模型和ML模型的混合组合，借助地理上广泛的WQ数据，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。该项目的重点是水的透明度，浮游植物生物量，和hypolimnetic缺氧的具体WQ指标，在解决问题：什么是主导过程的水质和它们如何在空间和时间上的变化？气候、土地利用和生态系统记忆如何相互作用，从局部到宏观系统尺度影响水质动态？湖泊水质变化的广泛空间和长期模式是什么？在解决这些问题，一个新的研究线启用在生态KGML模型湖WQ，这不仅是为了提高预测性能的WQ变量，但也可以导致发现新的知识WQ过程在一系列的时空尺度。新的研究估计过程参数的湖泊，鉴于其WQ观测，在计算效率和推广的方式探索使用ML方法。基于ML的WQ湖模型，使在每个湖泊的WQ变量之间的新的关系的发现，沿着提取相关的时间滞后。通过新的研究模块组成学习（MCL），生态KGML模型的开发，以确定哪些WQ过程是占主导地位的在一个给定的湖泊，以及它们如何相互作用，影响整体WQ动态。此外，生态KGML模型学习和区分特定于单个湖泊的过程，以及根据其生态特征概括各种类型湖泊的过程。这种对科学知识和数据的灵活和全面利用，使湖泊及其驱动因素之间的尺度依赖关系的研究成为可能，同时为湖泊提供跨多个时间和空间尺度的更可靠的预测。该奖项反映了NSF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。