Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models
Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models
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DOI：
10.1029/2023gl104464
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发表时间：
2023-09
影响因子：
5.2
通讯作者：
V. N. Tran;V. Ivanov;Donghui Xu;Jongho Kim
中科院分区：
文献类型：
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作者：
V. N. Tran;V. Ivanov;Donghui Xu;Jongho Kim
基于过程的模型（PBM）用于预测的应用受到多种不确定性和计算负担的干扰，导致明显的误差。为了解决这些挑战并应用于河流流量预测，开发了一种将高保真PBM与代理模型和机器学习（ML）模型相结合的新型建模框架。替代模型允许在最小精度损失的情况下提高PBM解决方案的计算效率。一种新的概率机器学习模型将PBM -代理预测误差划分为可约和不可约类型，量化它们的分布，这些分布是由明确感知的不确定性（如参数）或完全隐藏的不确定性（不包括或意外）引起的。使用这种方法，我们证明了在城市化流域的案例研究中，流量预测精度的显著提高。这种框架结合了高保真度和物理不可知模型的优势，为地球科学中广泛的预测问题提供了有效的解决方案。
Applications of process‐based models (PBM) for predictions are confounded by multiple uncertainties and computational burdens, resulting in appreciable errors. A novel modeling framework combining a high‐fidelity PBM with surrogate and machine learning (ML) models is developed to tackle these challenges and applied for streamflow prediction. A surrogate model permits high computational efficiency of a PBM solution at a minimum loss of its accuracy. A novel probabilistic ML model partitions the PBM‐surrogate prediction errors into reducible and irreducible types, quantifying their distributions that arise due to both explicitly perceived uncertainties (such as parametric) or those that are entirely hidden to the modeler (not included or unexpected). Using this approach, we demonstrate a substantial improvement of streamflow predictive accuracy for a case study urbanized watershed. Such a framework provides an efficient solution combining the strengths of high‐fidelity and physics‐agnostic models for a wide range of prediction problems in geosciences.