Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments

Zhong, L.; Lei, Huimin; Gao, Bing

doi:10.1029/2022wr034118

Cited by 24 publications

(12 citation statements)

References 99 publications

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“…Trained with samples supervised by PBM's high‐fidelity solutions, surrogate models can mirror outcomes of a broad spectrum of physical relations. This contrasts recently novel applications of an approach of physics‐informed ML (Bhasme et al., 2022; Frame et al., 2021; Lu et al., 2021; Zhong et al., 2023), in which simple constraints to a physics‐ignorant solution to maintain plausible ranges for modeled variables cannot attain the same level of prediction comprehensiveness as offered by the PBM‐to‐surrogate modeled dynamics. Under certain conditions however surrogate model performance can become inferior.…”

Section: Discussionmentioning

confidence: 87%

Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models

Tran,

Ivanov,

et al. 2023

Geophysical Research Letters

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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.

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Section: Discussionmentioning

confidence: 87%

Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models

Tran,

Ivanov,

et al. 2023

Geophysical Research Letters

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“…Recent developments in physics-wrapped neural networks have enhanced both interpretability and efficiency. The widely tested Process-Wrapped Recurrent Neural Network (P-RNN) approach is commonly applied alongside the EXP-HYDRO model [123] for parametrization or module replacement [122,[124][125][126].…”

Section: Replacing Process-based Model Modulesmentioning

confidence: 99%

“…These equations act as regularization agents during training, ensuring that the PINN parameters align with the embedded governing equations and enable accurate and efficient gradient computations with respect to model variables. Moreover, the fabric is flexible enough to incorporate expert knowledge or replace the required module [122,126,127,142].…”

Section: Emergence Physics-wrapped Neural Networkmentioning

confidence: 99%

“…Assessing streamflow responses to management scenarios and climate change is a critical area of research. However, both process-based and data-driven models can produce unreliable predictions [126,137]. Given the importance of the ET module in evaluating climate change impacts, enhancing prediction accuracy could involve either replacing the ET module in process-based models or incorporating simulated ET into a DDM framework.…”

Section: Applicability In Ungauged Regions and Assessment Of Natural ...mentioning

confidence: 99%

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Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review

Yifru,

Lim,

Lee

2024

Sustainability

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Streamflow prediction (SFP) constitutes a fundamental basis for reliable drought and flood forecasting, optimal reservoir management, and equitable water allocation. Despite significant advancements in the field, accurately predicting extreme events continues to be a persistent challenge due to complex surface and subsurface watershed processes. Therefore, in addition to the fundamental framework, numerous techniques have been used to enhance prediction accuracy and physical consistency. This work provides a well-organized review of more than two decades of efforts to enhance SFP in a physically consistent way using process modeling and flow domain knowledge. This review covers hydrograph analysis, baseflow separation, and process-based modeling (PBM) approaches. This paper provides an in-depth analysis of each technique and a discussion of their applications. Additionally, the existing techniques are categorized, revealing research gaps and promising avenues for future research. Overall, this review paper offers valuable insights into the current state of enhanced SFP within a physically consistent, domain knowledge-informed data-driven modeling framework.

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“…With the development of computer science, machine learning methods have been widely used in many fields [15][16][17][18][19][20], including agriculture [21,22]. Soil temperature research based on machine learning has also received much attention in recent years [14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau

Deng,

Liu,

Guo

et al. 2024

Agronomy

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Soil temperature directly affects the germination of seeds and the growth of crops. In order to accurately predict soil temperature, this study used RF and MLP to simulate shallow soil temperature, and then the shallow soil temperature with the best simulation effect will be used to predict the deep soil temperature. The models were forced by combinations of environmental factors, including daily air temperature (Tair), water vapor pressure (Pw), net radiation (Rn), and soil moisture (VWC), which were observed in the Hejiashan watershed on the Loess Plateau in China. The results showed that the accuracy of the model for predicting deep soil temperature proposed in this paper is higher than that of directly using environmental factors to predict deep soil temperature. In testing data, the range of MAE was 1.158–1.610 °C, the range of RMSE was 1.449–2.088 °C, the range of R2 was 0.665–0.928, and the range of KGE was 0.708–0.885 at different depths. The study not only provides a critical reference for predicting soil temperature but also helps people to better carry out agricultural production activities.

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Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments

Cited by 24 publications

References 99 publications

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

Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review

Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau

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