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. 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 performance of the models are evaluated and compared in case study for the Zahorska Lowland in the Slovak Republic. 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.
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Cisty, M. (2011). Determining Soil – Water Content by Data Driven Modeling When Relatively Small Data Sets Are Available. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN AIAI 2011 2011. IFIP Advances in Information and Communication Technology, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23957-1_33
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DOI: https://doi.org/10.1007/978-3-642-23957-1_33
Publisher Name: Springer, Berlin, Heidelberg
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