We combine physics-based groundwater reactive transport modeling with
machine learning techniques to quantify hydrogeologic model and solute
transport predictive uncertainties. We train an artificial neural
network (ANN) on a dataset of groundwater hydraulic heads and
H concentrations generated using a high-fidelity
groundwater reactive transport model. Using the trained ANN as a
surrogate model to reproduce the input-output response of the
high-fidelity reactive transport model, we quantify the posterior
distributions of hydrogeologic parameters and hydraulic forcing
conditions using Markov-chain Monte Carlo (MCMC) calibration against
field observations of groundwater hydraulic heads and
H concentrations. We demonstrate the methodology with
a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute
transport at a contaminated site in Wyoming, USA. Our results show that
including H observations in the calibration dataset
reduced the uncertainty in the estimated permeability field and
infiltration rates, compared to calibration against hydraulic heads
alone. However, predictive uncertainty quantification shows that CFC-12
transport predictions conditioned to the parameter posterior
distributions cannot reproduce the field measurements. We found that
calibrating the model to hydraulic head and H
observations results in groundwater mean ages that are too large to
explain the observed CFC-12 concentrations. The coupling of the
physics-based reactive transport model with the machine learning
surrogate model allows us to efficiently quantify model parameter and
predictive uncertainties, which is typically computationally intractable
using reactive transport models alone.
Use of a reactive transport model to describe reductive dechlorination (RD) as a remediation design tool: application at a CAH-contaminated site
