Physics‐Informed Neural Networks for Estimating a Continuous Form of the Soil Water Retention Curve From Basic Soil Properties

Physics‐Informed Neural Networks for Estimating a Continuous Form of the Soil Water Retention Curve From Basic Soil Properties

Abstract This paper presents a novel physics‐informed neural network (PINN) approach for developing pedotransfer functions (PTFs) to predict continuous soil water retention curves (SWRCs) based on soil textural fractions, organic carbon content, and bulk density. In contrast to conventional parametr...

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Main Authors: Sarem Norouzi, Charles Pesch, Emmanuel Arthur, Trine Norgaard, Per Moldrup, Mogens H. Greve, Amélie M. Beucher, Morteza Sadeghi, Marzieh Zaresourmanabad, Markus Tuller, Bo V. Iversen, Lis W. deJonge
Format: Article
Language:English
Published: Wiley 2025-03-01
Series:Water Resources Research
Subjects:
soil hydraulic properties
continuous pedotransfer functions
physics‐constrained machine learning
Online Access:https://doi.org/10.1029/2024WR038149
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