Global Sensitivity Analysis of Groundwater Related Dike Stability under Extreme Loading Conditions

Woerkom, Teun van; Beek, Rens van; Middelkoop, H.; Bierkens, Marc F. P.

doi:10.3390/w13213041

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…To efficiently guide these expenditures, extensive research is carried out to determine material properties of dikes (de Waal, 2016). This information is of great importance for dike stability, as the material type directly influences dike stability by the stability parameters (Cao et al, 2016), but also indirectly influences groundwater pressures and flow within the dike (Butera et al, 2020; Kuriqi et al, 2016; van Woerkom et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Assessing lithological uncertainty in dikes: Simulating construction history and its implications for flood safety assessment

Woerkom

Beek

Middelkoop

et al. 2022

J Flood Risk Management

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Dikes often have a long history of reinforcement, with each reinforcement adding new material resulting in a heterogeneous dike. As data on the dike internal heterogeneity is sparse, it is generally overlooked in the stability assessment of dikes. We present an object-based and process-based model simulating dike construction history on archeological dike cross, yielding similar patterns of heterogeneity as observed in real dikes, and apply it in a dike safety assessment. Model predictions improve when being based on more accurate statistics of dike buildup, or when being conditioned to ground truth data. When incorporated in a dike stability assessment, multiple model runs can be coupled to hydrological simulations and dike slope stability calculations, resulting in a probabilistic stability assessment considering internal dike heterogeneity. While high-resolution observations are still sparse, good model accuracies can be reached by combining regional information on dike buildup with local point observations and this model provides a parsimonious basis to include information of internal dike heterogeneity in safety assessments.

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Section: Introductionmentioning

confidence: 99%

Assessing lithological uncertainty in dikes: Simulating construction history and its implications for flood safety assessment

Woerkom

Beek

Middelkoop

et al. 2022

J Flood Risk Management

Self Cite

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Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco

El Mezouary,

Hadri,

Kharrou

et al. 2024

Appl Water Sci

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Groundwater resources in Morocco often face sustainability challenges due to increased exploitation and climate change. Specifically, the Al-Haouz-Mejjate groundwater in the Marrakesh region is faced with overexploitation and insufficient recharge. However, the complex subsurface geometries hamper hydrogeological modeling, characterization, and effective management. Reliably estimating aquifer substrate topography is critical for groundwater models but is challenged by limited direct measurements. This study develops nonlinear machine learning models to infer substrate depths by fusing sparse borehole logs with regional geospatial data. A Gaussian process regression approach provided robust holistic mapping, leveraging flexibility, and uncertainty quantification. Supplementary neural network architectures focus on isolating specific variable relationships, like surface elevation–substrate. Model accuracy exceeded 0.8 R-squared against validation boreholes. Spatial visualizations confirmed consistency across landscape transects. Elevation and piezometric data proved most predictive, though multivariate inputs were required for the lowest errors. The results highlight the power of statistical learning to extract meaningful patterns from disparate hydrological data. However, model opacity and the need for broader training datasets remain barriers. Overall, the work demonstrates advanced machine learning as a promising avenue for illuminating complex aquifer geometries essential for sustainability. Hybrid approaches that use both data-driven and physics-based methods can help solve long-standing problems with hydrogeological characterization.

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Effects of flood wave shape on probabilistic slope stability of dikes under transient groundwater conditions

Woerkom

Krogt

Bierkens

2023

Georisk: Assessment and Management of Risk for Engineered Syste

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Global Sensitivity Analysis of Groundwater Related Dike Stability under Extreme Loading Conditions

Cited by 3 publications

References 48 publications

Assessing lithological uncertainty in dikes: Simulating construction history and its implications for flood safety assessment

Assessing lithological uncertainty in dikes: Simulating construction history and its implications for flood safety assessment

Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco

Effects of flood wave shape on probabilistic slope stability of dikes under transient groundwater conditions

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