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In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models' development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models' estimations. Developed models are validated and compared with well-known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE = 0.018 m3/m3) that are significantly lower than those for the M77 (RMSE = 0.025 m3/m3) or KP87 (RMSE = 0. 047 m3/m3) models. [ABSTRACT FROM AUTHOR]

Particle size distribution (PSD) is a major soil characteristic, which is essential and commonly used for the development of pedotransfer functions (PTFs) to estimate the water retention of soils. The laser diffraction method (LDM) became a popular alternative to the standard sieve-hydrometer method (SHM) of PSD measurement. Unfortunately, PSDs determined with LDM and SHM methods differ sometimes substantially. Moreover, it is claimed that the laser diffraction method underestimates finer fractions in favor of coarser fractions. Several authors have tried to elaborate on methods to recalculate LDM PSD into its SHM counterparts, but no universal methodology has been developed to this date. In this paper, we use PSD determined by LDM directly for PTF development and compare it with the classical PTF approach based on PSD measured by SHM. Four different PTF models based on LDM particle size distribution data were developed, with different PSD characteristics taken as the models' input variables. The possibility of using alternative PSD characteristics, such as deciles, area moment mean and volume moment mean, for PTF development was examined. The accuracy of PTF models constructed on the basis of LDM-measured PSD was comparable with that of the developed models using texture data obtained from SHM, giving approximately the same RMSE and R2 values. Our study shows that LDM-measured particle size distribution may be directly used for PTF developments without any recalculations to their sieve-hydrometer counterparts. [ABSTRACT FROM AUTHOR]

Soil moisture prediction is an important application of environmental modeling. Here we compare two models that we used for soil moisture prediction: a soft-computing SVM model and a physical one based on Richards equation. Our results show that both of these models can successfully serve as soil moisture temporal estimation within the soil profile. Comparison of long-term daily prediction of soil water content at different depths of soil coming from two models, one physically based (HYDRUS-1D) and the other being soft-computing technique (Support Vector Machine), has been done. The models used provide quite accurate prediction for soil moisture content, especially in the most important layers for crop growth--those close to the surface. The RMSE of estimations of the volumetric soil water content in topsoil made by the Support Vector Machine (1/2-SVM) based model was 0.035, 0.030, and 0.021 cm cm for depths of 5, 10, and 25 cm, respectively, while the physically based 3'3 model had RMSE values of 0.042, 0.049, and 0.045 cm cm for the same depths. Our results showed that the 1/2-SVM modeling approach can be used for estimations of soil water content changes in time with the accuracy comparable of to the one of the physically based models. [ABSTRACT FROM AUTHOR]

The method of determination of the fractal dimension based on particle size distribution (PSD) derived from the laser diffraction method is analysed. Discussion is provided on the impact of the PSD determination methodology on the derived fractal dimension, and the possibility for characterization of soil by a single fractal approach. Analysisof particle size distribution (PSD) is one of the methods used for calculating fractal dimensions. The PSD of soils has been determined by sedimentation methods for many years. Newer laser diffractometry PSD measurement techniques were developed during the past few decades. Whereas sedimentation methods can differentiate between a few (usually eight) particle sizes, laser diffraction methods produce many more fractions (tens or even several hundred). The aim of this paper is to answer the question of whether or not the number of size fractions and intervals between them determine the value of the fractal dimension when it is calculated on the basis of PSD. The conclusion is that the number of fractions and the way in which they are divided does affect the calculated fractal dimension, and that therefore the calculation procedure should be standardized. [ABSTRACT FROM AUTHOR]