利用深度神经运算器架起水文集合模拟与学习的桥梁

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES
Alexander Y. Sun, Peishi Jiang, Pin Shuai, Xingyuan Chen
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引用次数: 0

摘要

长期以来,基于集合的模拟和学习(ESnL)一直被用于水文参数推断,但基于过程的 ESnL 的计算要求可能相当高。为解决这一问题,我们提出了一种深度神经算子学习方法。神经算子是一种通用的机器学习算法,可以学习无限维空间之间的函数映射,为科学机器学习提供了一种高度灵活的工具。我们的方法基于 DeepONet(一种特定的深度神经算子),旨在解决水文学中的几个常见问题,即模型参数估计、无测站位置预测和不确定性量化。在此,我们利用为美国东部流域创建的现有大型模型集合,展示了基于 DeepONet 的工作流程的有效性。结果表明,DeepONet 从模型集合中学习 ML 代理模型的效率很高,在保留测试集上的修正 Kling-Gupta 效率超过了 0.9。使用训练好的 DeepONet 代用模型和遗传算法进行参数推理,也产生了稳健的结果。此外,我们还建立并训练了一个单独的 DeepONet 模型,用于以物理信息为基础的序列到序列流预测,这进一步减少了预先训练的 DeepONet 代用模型的偏差。虽然这项研究主要侧重于单一流域,但我们的方法具有通用性,可以扩展到多个流域或模型的模型集合中进行学习。因此,这项研究对混合机器学习在水文学中的应用做出了重大贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators
Ensemble-based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process-based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite-dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet-based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling-Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics-informed, seq-to-seq streamflow forecasting, which further reduces biases in the pre-trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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