Physics-structured model restores corrupted soil loss factors
PhyRestore: Physics-Structured Latent-Factor Restoration
Machine Learning
Summary
Predicting how soil loss changes over time is hard when the factors used in calculations are noisy or broken. The researchers introduce PhyRestore, a method that fixes these corrupted factors by using knowledge of the physics behind soil loss. Instead of guessing soil loss directly, PhyRestore repairs the basic factors and then calculates the change from those. Their tests show that this method works best when the corrupted factors are still recognizable, but less well when multiple factors are corrupted, or when unusual extreme values occur.
What this means in practice
- •For environmental modelers: Improve soil erosion estimates by restoring corrupted input factors before applying physical equations.
- •For landscape management teams: Better assess areas with rare but significant soil loss changes by using physics-based restoration of degraded data.
Authors
Ahmed Shafee, Chayan Lahiri
Abstract
Estimating temporal soil-loss change is challenging when physically meaningful input factors are noisy or corrupted, particularly because substantial changes are rare relative to the large number of locations exhibiting little change. We study this problem through the Revised Universal Soil Loss Equation (RUSLE) and introduce PhyRestore, a physics-structured latent-factor restoration framework. Rather than directly predicting soil-loss change or correcting a degraded physical estimate, PhyRestore restores corrupted physical factors and reconstructs temporal change through the known physical relationship. We evaluate PhyRestore in a watershed-scale bitemporal raster setting under isolated and simultaneous corruption of rainfall erosivity and cover management, comparing it with the degraded RUSLE estimate and Direct RF, XGBoost, MLP, and CNN models. Factor restoration improves high-magnitude recovery when the corrupted factors remain identifiable, but its advantage weakens under joint corruption, sparse positive extremes, and factor values outside the training support.