Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review
The growing number of contaminated sites across the world pose a considerable threat to
the environment and human health. Remediating such sites is a cumbersome process with …
the environment and human health. Remediating such sites is a cumbersome process with …
Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence
Groundwater models are essential for understanding aquifer systems behavior and effective
water resources spatio-temporal distributions, yet they are often hindered by challenges …
water resources spatio-temporal distributions, yet they are often hindered by challenges …
An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers
Accurately estimating high-dimensional permeability (k) fields through data assimilation is
critical for minimizing uncertainties in groundwater flow and solute transport simulations …
critical for minimizing uncertainties in groundwater flow and solute transport simulations …
Efficient estimation of groundwater contaminant source and hydraulic conductivity by an ILUES framework combining GAN and CNN
N Zheng, S Jiang, X ** and monitoring of DNAPL source zones with combined direct current resistivity and induced polarization: a field‐scale numerical investigation
Direct current (DC) resistivity has been widely investigated for non‐invasive map** of
dense non‐aqueous phase liquids (DNAPLs); however, due to its difficulty in distinguishing …
dense non‐aqueous phase liquids (DNAPLs); however, due to its difficulty in distinguishing …
Characterization of the non-Gaussian hydraulic conductivity field via deep learning-based inversion of hydraulic-head and self-potential data
Accurate characterization of the spatial heterogeneity of hydraulic properties such as
hydraulic conductivity (K) is essential for understanding groundwater flow and contaminant …
hydraulic conductivity (K) is essential for understanding groundwater flow and contaminant …
[HTML][HTML] Ensemble Kalman filter for GAN-ConvLSTM based long lead-time forecasting
Data-driven machine learning techniques have been increasingly utilized for accelerating
nonlinear dynamic system prediction. However, machine learning-based models for long …
nonlinear dynamic system prediction. However, machine learning-based models for long …
Hydrogeophysical Inversion of Time‐Lapse ERT Data to Determine Hillslope Subsurface Hydraulic Properties
Time‐lapse electrical resistivity tomography (ERT) data are increasingly used to inform the
hydrologic dynamics of mountainous environments at the hillslope scale. Despite their …
hydrologic dynamics of mountainous environments at the hillslope scale. Despite their …