Collaborative Research: Informing River Corridor Transport Modeling by Harnessing Community Data and Physics-Aware Machine Learning

合作研究：通过利用社区数据和物理感知机器学习为河流走廊交通建模提供信息

• 批准号: 2142691
• 负责人: Ricardo Gonzalez-Pinzon
• 金额: $ 25.42万
• 依托单位: University of New Mexico
• 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: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2142691&HistoricalAwards=false
• 关键词: Collaborative; Research; Informing; River; Corridor

River corridors, including their adjacent and underlying sediments, are ecosystems where waters from different sources mix. This mixing controls the fate of a multitude of dissolved solutes, such as nutrients essential to the ecosystem, dissolved minerals from natural weathering, pharmaceuticals from wastewater treatment plant discharge, and contaminants from nearby sources. Practically useful computer models of how solutes are transported, including how they are exchanged back and forth between riverbed sediments and the river itself, are needed to understand water quality in rivers. Recent research suggests that these transport processes are missed by state-of-the-art computer models. This project will develop a general approach to building adaptable computer models based on recently developed tools in mathematical modeling, including artificial intelligence, to investigate how to specialize general models for particular rivers. The project will generate a large database of experimental results from river transport studies from around the globe. The database will be used to extract patterns associated with solute transport and will be disseminated broadly with the scientific community. The project team will host annual workshops to enhance database sharing, distribute educational modules on the use of artificial intelligence in hydrological sciences, and discuss approaches to standardize data collection. The goals of this project are to develop a comprehensive database of river tracer testing data for open sharing with the scientific community, and to develop and test a novel generalized model of solute transport in river corridors. The activities proposed center around the construction of a community-available, large database of tracer tests performed in streams and rivers worldwide, and its use as curricula for machine learning of model properties. Congruent data analytics will be performed to identify correlations among key variables of both river and tracer test properties, treating breakthrough curves not individually but in the tracer test sets in which they are measured. Uncertainty in experimentally measured solute concentrations will be formally addressed and used to describe model predictive power. The models selected for evaluation range from the classical transient storage model to a new model designed to address the hypothesis that residence time in the river and in the hyporheic zone both matter to exchange fluxes. Both conventional inverse modeling and machine learning tools will be applied in dual model calibration tasks, bringing uniquely powerful physics-informed neural networks to bear on this challenging problem.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.

河流走廊，包括其相邻和潜在的沉积物，是来自不同来源的水混合的生态系统。这种混合控制了多种溶解溶质的命运，例如生态系统所必需的营养，自然风化的溶解矿物质，废水处理厂放电的药物以及附近来源的污染物。实际上，需要如何在河床沉积物和河流本身之间来回交换溶质的计算机模型，以了解河流的水质。 最近的研究表明，这些运输过程被最先进的计算机模型遗漏了。该项目将开发一种基于包括人工智能在内的数学建模工具（包括人工智能）的最新开发的工具来构建适应性计算机模型的通用方法，以调查如何专门为特定河流的通用模型。该项目将从全球的河流运输研究中产生大量的实验结果数据库。该数据库将用于提取与溶质运输相关的模式，并将与科学界大致传播。项目团队将举办年度研讨会，以增强数据库共享，分发有关在水文科学中使用人工智能的教育模块，并讨论标准化数据收集的方法。该项目的目标是开发河流示踪剂测试数据的综合数据库，以与科学界的公开共享，并开发和测试一种新型的河流走廊溶质运输模型。这些活动围绕着在全球溪流和河流中执行的社区可用的大型示踪剂测试数据库的构建，并用作模型属性的机器学习课程。将执行一致的数据分析，以识别河流和示踪剂测试属性的关键变量之间的相关性，从而不单独地处理突破性曲线，而是在测量它们的示踪剂测试集中。实验测量的溶质浓度的不确定性将被正式解决，并用于描述模型预测能力。从经典的瞬态存储模型到一个新模型，旨在解决河流中停留时间和在低音区域的新模型，都选择了用于评估范围的模型。 传统的逆建模和机器学习工具都将应用于双重模型校准任务，这使得独特的物理知识的神经网络在这个具有挑战性的问题上都可以解决。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持的。