Physics-Informed Deep Learning for Soil Water Dynamics

The prediction of soil moisture movement remains challenging due to the complexity of underground flow processes and the availability of accurate soil parameters. There have been attempts to overcome this issue with parametric models and inverse modeling, but it remains challenging because it requires knowledge of initial and boundary conditions. While deep learning offers a solution, the one significant constraint remains not to violate the physical constraints. I present a novel physics-informed neural network (PINN) framework that integrates the soil moisture movement governing equation constraints with deep learning to predict soil moisture dynamics. The new approach follows mass conservation principles and soil hydraulic properties into the neural network's loss function. The model ensures physically consistent predictions. The framework simultaneously learns soil hydraulic parameters and water content distributions, adapting to heterogeneous soil conditions through a hybrid optimization strategy. The model incorporates the Van Genuchten parameterization within the physics-informed architecture to ensure consistency and accuracy. This methodology bridges the gap between computationally intensive traditional numerical solutions and pure data-driven approaches, offering a new paradigm for modeling soil water dynamics.