Investigating steady unconfined groundwater flow using Physics Informed Neural Networks

Shadab, Mohammad Afzal; Luo, Dingcheng; Hiatt, Eric; Shen, Yiran; Hesse, M. A.

doi:10.1016/j.advwatres.2023.104445

Cited by 9 publications

(1 citation statement)

References 34 publications

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“…Depina et al [31] utilized PINNs to calculate unsaturated groundwater flow by making use of Richards' equation to obtain the parameters of the van Genuchten model. Shadab et al [32] predicted phreatic surface profiles using PINNs.…”

Section: Introductionmentioning

confidence: 99%

A Data Assimilation Methodology to Analyze the Unsaturated Seepage of an Earth–Rockfill Dam Using Physics-Informed Neural Networks Based on Hybrid Constraints

Dai,

Zhou,

et al. 2024

Water

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Data assimilation for unconfined seepage analysis has faced significant challenges due to hybrid causes, such as sparse measurements, heterogeneity of porous media, and computationally expensive forward models. To address these bottlenecks, this paper introduces a physics-informed neural network (PINN) model to resolve the data assimilation problem for seepage analysis of unsaturated earth–rockfill dams. This strategy offers a solution that decreases the reliance on numerical models and enables an accurate and efficient prediction of seepage parameters for complex models in the case of sparse observational data. For the first attempt in this study, the observed values are obtained by random sampling of numerical solutions, which are then contributed to the synchronous constraints in the loss function by informing both the seepage control equations and boundary conditions. To minimize the effects of sharp gradient shifts in seepage parameters within the research domain, a residual adaptive refinement (RAR) constraint is introduced to strategically allocate training points around positions with significant residuals in partial differential equations (PDEs), which could facilitate enhancing the prediction accuracy. The model’s effectiveness and precision are evaluated by analyzing the proposed strategy against the numerical solutions. The results indicate that even with limited sparse data, the PINN model has great potential to predict seepage data and identify complex structures and anomalies inside the dam. By incorporating coupling constraints, the validity of our PINN model could lead to theoretically viable applications of hydrogeophysical inversion or multi-parameter seepage inversion. The results show that the proposed framework can predict the seepage parameters for the entire research domain with only a small amount of observation data. Furthermore, with a small amount of observation data, PINNs are able to obtain more accurate results than purely data-driven DNNs.

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Section: Introductionmentioning

confidence: 99%

A Data Assimilation Methodology to Analyze the Unsaturated Seepage of an Earth–Rockfill Dam Using Physics-Informed Neural Networks Based on Hybrid Constraints

Dai,

Zhou,

et al. 2024

Water

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Exploring Simulation–Optimization for Sustainable Groundwater Management: A Critical Review

Mishra,

Gaur,

Kacem

et al. 2024

CLEAN Soil Air Water

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Simulation–optimization (S–O) is a well‐regarded method for solving groundwater (GW) management problems. Although S–O has significantly improved the decision support system for GW management, it still lacks practical applicability. As a result, many researchers have been improving its components, leading to slightly or significantly better performance. To understand these challenges efficiently, this article delves into principal components of S–O that offer in‐depth critical insights into GW's sustainability. The discussed segments are divided into simulation models, optimization methods, categories and conceptualization of management problems, and the formulation of real‐world objective functions. This review also examines surrogate‐assisted simulation models to reduce computational challenges. Methods to address model uncertainty and decision‐making in applying S–O for sustained yield problems are addressed. The review outlays critical steps in S–O methodology and recommends potential research directions to aid researchers in further enhancing the practicality of S–O.

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Exploring the power of data-driven models for groundwater system conceptualization: a case study of the Grazer Feld Aquifer, Austria

Kokimova,

Collenteur,

Birk

2024

Hydrogeol J

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Developing a reliable conceptual model is crucial for analyzing groundwater systems. An essential part of the aquifer conceptualization is the identification of the hydrological stresses that control the hydraulic head fluctuations. By effectively capturing and understanding these stresses, the propagation of potential errors and uncertainties through subsequent modeling steps can be minimized. This study aims to test data-driven models as screening models for conceptualizing a groundwater system. The case study is applied to the Grazer Feld Aquifer in southeast Austria. Time series models are applied to: (1) identify the stresses likely influencing the observed head fluctuations and their spatial variability; (2) identify locations where a lack of understanding of head fluctuations exists; and (3) discuss the limitations and opportunities associated with data-driven models to support system conceptualization. Time series models were created for 144 monitoring wells where sufficient head observations were available during the calibration period (2005–2015). A total of 576 models were developed, incorporating the combinations of stresses: recharge, river level, and a step trend. Following the model selection process, each model was categorized based on its performance and divided into four groups. At 88 sites, recharge and river level variations were identified as the primary controlling stresses influencing head fluctuations. The inclusion of the step trend was found to be necessary at five sites to accurately simulate heads due to dam construction. The application of data-driven models in this study enhanced the identification of key aquifer stresses, facilitating a more informed understanding of the groundwater system.

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Investigating steady unconfined groundwater flow using Physics Informed Neural Networks

Cited by 9 publications

References 34 publications

A Data Assimilation Methodology to Analyze the Unsaturated Seepage of an Earth–Rockfill Dam Using Physics-Informed Neural Networks Based on Hybrid Constraints

A Data Assimilation Methodology to Analyze the Unsaturated Seepage of an Earth–Rockfill Dam Using Physics-Informed Neural Networks Based on Hybrid Constraints

Exploring Simulation–Optimization for Sustainable Groundwater Management: A Critical Review

Exploring the power of data-driven models for groundwater system conceptualization: a case study of the Grazer Feld Aquifer, Austria

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