利用卷积神经网络引导的动态维度搜索方法优化水文模型参数估计

IF 4 2区 环境科学与生态学 Q1 WATER RESOURCES Advances in Water Resources Pub Date : 2024-11-01 DOI:10.1016/j.advwatres.2024.104842
Ashlin Ann Alexander , D. Nagesh Kumar
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引用次数: 0

摘要

水文模型校准在估算最佳参数以进行精确模拟方面起着至关重要的作用。由于大多数参数都是对物理过程的概念性描述,因此直接测量参数是水文模型中不可避免的挑战。建模人员通常采用优化算法来校准水文模型。然而,这些算法通常会带来计算上的挑战,尤其是在处理复杂的物理模型和分布式模型时。为了应对这些挑战,我们的研究引入了一种名为 hydroCNN+DDS 的新方法。通过利用卷积神经网络(CNN)和动态维度搜索(DDS)算法的优势,hydroCNN+DDS 简化了复杂物理模型的模型校准过程。这种方法能够捕捉到排放时间序列和参数之间的一般模式和关系,而不会影响基本物理特性。我们使用 hydroCNN+DDS 估算高度参数化的水文模型--Structure for Unifying Multiple Modeling Alternatives (SUMMA) 中的参数,使用的是每小时观测到的排水量。值得注意的是,hydroCNN 能够快速生成次优参数,为 DDS 提供良好的初始解决方案。这种初始化有助于 DDS 更快地趋向最优解。水文 CNN+DDS 方法的显著优势之一是其潜在的空间和时间可转移性。这一特点在动态系统和历史数据有限的地区非常有价值,扩大了该方法的适用范围。此外,我们提出的方法用途广泛,可应用于任何简单或复杂的模型,并能适应任何感兴趣的变量。我们的方法遵循了良好模型校准的最佳实践。
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Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach
Hydrological model calibration plays a crucial role in estimating optimal parameters for accurate simulation. Estimation of parameters is inevitable in hydrological modeling due to the challenge of directly measuring them, as most parameters are conceptual descriptions of physical processes. Modelers commonly employ optimization algorithms for calibrating hydrological models. However, these algorithms often pose computational challenges, especially when dealing with complex physics-based and distributed models. To address these challenges, our study introduces a novel approach called hydroCNN+DDS. By leveraging the strengths of Convolutional Neural Networks (CNN) and the Dynamically Dimensioned Search (DDS) algorithm, hydroCNN+DDS simplifies the model calibration process in complex physics-based models. This approach enables to capture the general patterns and relationships between discharge time series and parameters without compromising the underlying physics. We use hydroCNN+DDS to estimate parameters in the highly parameterized hydrological model, Structure for Unifying Multiple Modeling Alternatives (SUMMA) using hourly observed discharge. Notably, hydroCNN quickly generates sub-optimal parameters, serving as a good initial solution for DDS. This initialization aids DDS in converging faster towards an optimal solution. One of the notable advantages of the hydroCNN+DDS approach is its potential for spatial and temporal transferability. This feature proves valuable in dynamic systems and regions with limited historical data, expanding the applicability of the methodology. Furthermore, our proposed methodology is versatile and can be applied to any simple or complex models, accommodating any variables of interest. The best practices of good model calibration are followed in our approach.
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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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