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

河流廊道，包括其相邻和下面的沉积物，是不同来源的水混合的生态系统。这种混合控制了大量溶解溶质的命运，例如生态系统必需的营养物质、自然风化产生的溶解矿物质、废水处理厂排放的药物以及附近来源的污染物。要了解河流的水质，就需要实用的计算机模型来研究溶质是如何运输的，包括它们是如何在河床沉积物和河流本身之间来回交换的。最近的研究表明，最先进的计算机模型忽略了这些传输过程。该项目将开发一种通用方法，基于最近开发的数学建模工具（包括人工智能），建立适应性强的计算机模型，以研究如何为特定河流专门设计通用模型。该项目将产生一个大型数据库，其中包括来自全球的河流运输研究的实验结果。该数据库将用于提取与溶质迁移有关的模式，并将在科学界广泛传播。项目团队将举办年度研讨会，以加强数据库共享，分发人工智能在水文科学中的应用教育模块，并讨论标准化数据收集的方法。该项目的目标是建立一个全面的河流示踪剂测试数据数据库，与科学界开放共享，并开发和测试河流走廊中溶质运输的新型广义模型。拟议的活动主要围绕建立一个社区可用的大型数据库，其中包括在世界各地的溪流和河流中进行的示踪剂测试，并将其用作模型属性机器学习的课程。将执行一致数据分析，以确定河流和示踪剂测试属性的关键变量之间的相关性，不是单独处理突破曲线，而是在测量它们的示踪剂测试集中处理突破曲线。实验测量的溶质浓度的不确定性将被正式处理，并用于描述模型的预测能力。所选择的评估模型从经典的暂态存储模型到设计的一个新模型，该模型旨在解决在河流中停留时间和在潜流区停留时间都对交换通量有影响的假设。传统的逆建模和机器学习工具都将应用于双模型校准任务，带来独特的强大的物理信息神经网络来承担这一具有挑战性的问题。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。

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.