根据数据自动生成可解释的降雨-径流模型

IF 4 2区 环境科学与生态学 Q1 WATER RESOURCES
Travis Adrian Dantzer, Branko Kerkez
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引用次数: 0

摘要

突然激增的数据给水管理带来了新的挑战,包括质量控制、同化和分析。很少有方法能将不断增长的数据量整合成可解释的结果。基于过程的水文模型在设计上并不适合消耗大量数据。另外,新的机器学习工具可以自动进行数据分析和预测,但它们缺乏可解释性,而且依赖于非常庞大的数据集,这限制了洞察力的发现,并可能影响信任度。为了弥补这一不足,我们提出了一种新方法,力求在基于流程的建模和基于数据的建模之间找到一个中间点。这项工作的贡献在于,它是一种自动化、可扩展的方法,只需使用降雨和径流测量数据,就能发现水文系统中的微分方程和潜在状态估计。我们展示了这一方法如何使自动化工具仅通过测量数据就能学习 6 到 18 个参数的可解释模型。我们将这种方法应用于全美近 400 个溪流测量站点,展示了如何仅通过降雨和径流测量数据就能重建复杂的集水动态。我们还展示了这种方法是如何发现替代模型的,这些模型可以复制更为复杂的基于过程的模型的动态变化,但计算复杂度仅为模型的一小部分。我们讨论了由此产生的流域动态表示如何提供洞察力和计算效率,以实现大型传感器网络的自动预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generating interpretable rainfall-runoff models automatically from data

A sudden surge of data has created new challenges in water management, spanning quality control, assimilation, and analysis. Few approaches are available to integrate growing volumes of data into interpretable results. Process-based hydrologic models have not been designed to consume large amounts of data. Alternatively, new machine learning tools can automate data analysis and forecasting, but their lack of interpretability and reliance on very large data sets limits the discovery of insights and may impact trust. To address this gap, we present a new approach, which seeks to strike a middle ground between process-, and data-based modeling. The contribution of this work is an automated and scalable methodology that discovers differential equations and latent state estimations within hydrologic systems using only rainfall and runoff measurements. We show how this enables automated tools to learn interpretable models of 6 to 18 parameters solely from measurements. We apply this approach to nearly 400 stream gaging sites across the US, showing how complex catchment dynamics can be reconstructed solely from rainfall and runoff measurements. We also show how the approach discovers surrogate models that can replicate the dynamics of a much more complex process-based model, but at a fraction of the computational complexity. We discuss how the resulting representation of watershed dynamics provides insight and computational efficiency to enable automated predictions across large sensor networks.

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来源期刊
Advances in Water Resources
Advances in Water Resources 环境科学-水资源
CiteScore
9.40
自引率
6.40%
发文量
171
审稿时长
36 days
期刊介绍: Advances in Water Resources provides a forum for the presentation of fundamental scientific advances in the understanding of water resources systems. The scope of Advances in Water Resources includes any combination of theoretical, computational, and experimental approaches used to advance fundamental understanding of surface or subsurface water resources systems or the interaction of these systems with the atmosphere, geosphere, biosphere, and human societies. Manuscripts involving case studies that do not attempt to reach broader conclusions, research on engineering design, applied hydraulics, or water quality and treatment, as well as applications of existing knowledge that do not advance fundamental understanding of hydrological processes, are not appropriate for Advances in Water Resources. Examples of appropriate topical areas that will be considered include the following: • Surface and subsurface hydrology • Hydrometeorology • Environmental fluid dynamics • Ecohydrology and ecohydrodynamics • Multiphase transport phenomena in porous media • Fluid flow and species transport and reaction processes
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