An Improved Van Genuchten Soil Water Characteristic Model Under Multi-Factor Coupling and Machine Learning-Based Parameter Prediction

Accurately constructing a soil–water characteristic curve (SWCC) model that accounts for the combined effects of multiple factors is of great significance for in-depth understanding of the physical and mechanical behaviors of soils in complex environments. Based on the van Genuchten (vG) model, this study systematically analyzed the effect of the coupling mechanism of void ratio, temperature, and salinity on SWCC. An SWCC model capable of characterizing multi-factor coupling effects was established by incorporating multi-factor influence terms. Fitting verification with experimental data demonstrates that the proposed model can effectively depict soil water retention characteristics under the combined action of multiple factors. Furthermore, parameter sensitivity analysis clarifies the influence laws of each model parameter on the air entry value and the slope of the transition segment of SWCC. To address the challenge of cumbersome determination of model parameters, a parameter prediction method based on the Bayesian regularized neural network (BRNN) was proposed. By training a large volume of SWCC experimental data under multi-factor conditions, effective prediction of model parameters was achieved, with the input being the basic physical properties of soil and environmental variables and the output being the target model parameters. Considering that the influence of salinity introduces additional parameters, the training set was divided into two scenarios (saline and non-saline conditions) for separate modeling to enhance the pertinence and accuracy of parameter prediction. Prediction results indicate that the proposed method exhibits reliable parameter prediction capability, and its prediction accuracy is mainly influenced by the quantity and quality of training data.