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

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

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...

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Bibliographic Details
Main Authors: Xueyuan Kang, Amalia Kokkinaki, Xiaoqing Shi, Jonghyun Lee, Zhilin Guo, Lingling Ni, Jichun Wu
Format: Article
Language:English
Published: Wiley 2024-07-01
Series:Water Resources Research
Subjects:
DNAPL
contaminant mass discharge
upscaled dissolution model
Bayesian neural network
explainable artificial intelligence
Online Access:https://doi.org/10.1029/2023WR036864
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https://doi.org/10.1029/2023WR036864

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