Evaluation of Water Retention Curves by Regression and Machine Learning Methods

The paper presents two methods that simplify the estimation of the water retention curves. The case study is evaluated for the soils of Záhorská lowland in the paper. These methods are based on the supposed dependence of the soil water content on the percentage content of the 1st, 2nd, 3rd and 4th Kopecký grain categories, and the dry bulk density. The representative set of the drying branch of water retention curves was measured using soil samples from the Záhorská lowland region in a laboratory. Particle size distribution and dry bulk density were also determined. In this paper support vector machines and multiple linear regression is compared to estimate the pedotransfer functions that can be used for the prediction of the drying branch of the water retention curve. Both methods were verified on other data set of measured water retention curves than the one which was used for building the models with a close agreement to measured results.

Pore-scale investigation of dynamic effects on the moisture retention curve during spontaneous imbibition

The moisture retention curve of porous materials is often assumed to be independent of the process dynamics, i.e., of the drying/wetting rate. Experimental outcomes and pore-scale simulations put this assumption into question though. It has been shown that dynamic effects can significantly affect the moisture retention curve, which presents different behaviours, depending on whether it is determined at transient or steady-state conditions. The cause of this phenomenon is addressed as “dynamic effects” in the literature. While dynamic effects of the drainage process have been widely studied, the data concerning spontaneous imbibition are still quite limited. We attempt at reducing this lack of knowledge by modelling spontaneous imbibition in an artificial material sample represented by a pore network model. In our model, the liquid flow is described via the Hagen-Poiseuille equation, while a percolation algorithm controls the dynamics of liquid-gas interfaces through the network junctions. A dynamic contact angle between liquid water and pore surface is considered, depending on the velocity of the meniscus. Dynamic states are determined by linking the local capillary pressure to the local moisture content in the artificial material sample subject to spontaneous imbibition. Our investigation demonstrates that dynamic effects due to contact angle variations may have a major impact on the imbibition process.