2024
DOI: 10.1029/2023wr036864
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Modeling Upscaled Mass Discharge From Complex DNAPL Source Zones Using a Bayesian Neural Network: Prediction Accuracy, Uncertainty Quantification and Source Zone Feature Importance

Xueyuan Kang,
Amalia Kokkinaki,
Xiaoqing Shi
et al.

Abstract: The mass discharge emanating from dense non‐aqueous phase liquid (DNAPL) source zones (SZs) is often used as a key metric for risk assessment. To predict the temporal evolution of mass discharge, upscaled models have been developed to approximate the relationship between the depletion of SZ and the mass discharge. A significant challenge stems from the choice of the SZ parameterization, so that a limited number of domain‐averaged SZ metrics can suffice as an input and accurately predict the complex mass‐discha… Show more

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