Determining Soil – Water Content by Data Driven Modeling When Relatively Small Data Sets Are Available

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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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.289-295, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_33〉
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Milan Cisty. Determining Soil – Water Content by Data Driven Modeling When Relatively Small Data Sets Are Available. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.289-295, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_33〉. 〈hal-01571368〉

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