Indirect models for SWCC parameters: reducing prediction uncertainty with machine learning

The soil-water characteristic curve (SWCC) is crucial for modelling the transport of water and hazardous materials in the vadose zone. However, measuring SWCC is often cumbersome and time-consuming. This paper introduces indirect models that predict SWCC parameters in probabilistic distributions using easily measurable quantities such as particle-size distributions and porosity. This paper starts with building a joint normal model and the derived conditional probability from it serves as a predictive model. However, this model had extremely high prediction uncertainty. To reduce such uncertainty, various machine-learning techniques were explored, including introducing the dependence of variation scale on predictors, using artificial neural networks (ANN) to model nonlinear dependence, incorporating additional predictive features, and generating a larger dataset. The final machine-learning model successfully reduces prediction variability and has been rigorously tested on a separate set of samples to prevent overfitting.