Predicting Soil Moisture Dynamics with Physics-Informed Neural Networks
A PINN predicting continuous 1D soil moisture profiles for precision irrigation, integrating the Richards equation as a physics constraint. Achieved PDE residuals of 1×10−7 and was validated against sensor data from 57 stations across 8 global soil moisture networks (RMSE 0.018 m3/m3). Transfer learning cut training time by more than half while enabling 24–72 hour predictions where traditional numerical solvers failed to converge.
Keywords: PyTorch, Julia, PINNs, Richards equation, transfer learning.