<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>A key physical property used in the description of a soil-water regime is a soil water retention curve, which shows the relationship between the water content and the water potential of the soil. Pedotransfer functions are based on the supposed dependence of the soil water content on the available soil characteristics, e.g., on the relative content of the particle size in the soil and the dry bulk density of the soil. This dependence could be extracted from the available data by various regression methods. In this paper, artificial neural networks (ANNs) and support vector machines (SVMs) were used to estimate a drying branch of a water retention curve. The paper compares the mentioned methods by estimating the water retention curves on regional scale for the Záhorská lowland in the Slovak Republic, where relatively small data set was available. The performance of the models was evaluated and compared. These computations did not fully confirm the superiority of SVMs over ANNs as is often proclaimed in the literature, because the results obtained show that in this study the ANN model performs somewhat better and is easier to handle in determining pedotransfer functions than the SVM models. Nevertheless, the results from both data-driven models are quite close, and the results show that they provide a significantly more precise outcome than a traditional multi-linear regression does.</p></abstract>

A key physical property used in the description of a soil-water regime is a soil water retention curve, which shows the relationship between the water content and the water potential of the soil. Pedotransfer functions are based on the supposed dependence of the soil water content on the available soil characteristics, e.g., on the relative content of the particle size in the soil and the dry bulk density of the soil. This dependence could be extracted from the available data by various regression methods. In this paper, artificial neural networks (ANNs) and support vector machines (SVMs) were used to estimate a drying branch of a water retention curve. The paper compares the mentioned methods by estimating the water retention curves on regional scale for the Záhorská lowland in the Slovak Republic, where relatively small data set was available. The performance of the models was evaluated and compared. These computations did not fully confirm the superiority of SVMs over ANNs as is often proclaimed in the literature, because the results obtained show that in this study the ANN model performs somewhat better and is easier to handle in determining pedotransfer functions than the SVM models. Nevertheless, the results from both data-driven models are quite close, and the results show that they provide a significantly more precise outcome than a traditional multi-linear regression does.

Comparison of Three Regression Models for Determining Water Retention Curves

Faculty of Civil Engineering, Slovak University of Technology Bratislava, Radlinského 11, 813 68 Bratislava, Slovak Republic

Journal of Hydrology and Hydromechanics

{"article-title":"Comparison of Three Regression Models for Determining Water Retention Curves"}

A key physical property used in the description of a soil-water regime is a soil water retention curve, which shows the relationship between the water content...

Comparison of Three Regression Models for Determining Water Retention Curves

Data publikacji: 30 lis 2011

Zakres stron: 275 - 284

DOI: https://doi.org/10.2478/v10098-011-0023-7

Słowa kluczowe
retenčná krivka, pedotransférová funkcia, neurónová sieť, mechanizmus podporných vektorov, viacnásobná lineárna regresia

This content is open access.

Comparison of Three Regression Models for Determining Water Retention Curves

Data publikacji: 30 lis 2011

Zakres stron: 275 - 284

DOI: https://doi.org/10.2478/v10098-011-0023-7

Słowa kluczoweretenčná krivka, pedotransférová funkcia, neurónová sieť, mechanizmus podporných vektorov, viacnásobná lineárna regresia

This content is open access.

Słowa kluczowe
retenčná krivka, pedotransférová funkcia, neurónová sieť, mechanizmus podporných vektorov, viacnásobná lineárna regresia