Groundwater flow and contamination studies
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An improved diffusion-convection-reaction model is established that considers the convection velocity as a function of both time and space and a Physics-Informed Neural Network framework is constructed to solve the model.
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In response to the issue in traditional groundwater pollutant transport models where the convection velocity is treated as a constant, making it difficult to describe actual non-uniform transport processes, this paper establishes an improved diffusion-convection-reaction model that considers the convection velocity as a function of both time and space. Based on this governing equation, a Physics-Informed Neural Network (PINN) framework is constructed to solve the model. The partial differential equations and initial/boundary conditions are embedded into the network training process to solve for the spatiotemporal distribution of pollutant concentrations. The model is validated through vertical infiltration experiments in a soil column. The results show that the pollutant transport trends predicted by the model are in good agreement with the experimental observations, demonstrating the effectiveness of the proposed model and methodology.
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@article{Wang2026Solving,
title = {Solving Groundwater Pollutant Transport Model by Physics-Informed Neural Networks},
author = {Zhaoche Wang},
journal = {Theoretical and Natural Science},
year = {2026},
doi = {10.54254/2753-8818/2026.35310},
url = {https://doi.org/10.54254/2753-8818/2026.35310}
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