Developing Pseudo Continuous Pedotransfer Functions for International Soils Measured with the Evaporation Method and the HYPROP System: I. The Soil Water Retention Curve
| Autor: | Hasan Sabri Öztürk, Amir Haghverdi, Amninder Singh, Wolfgang Durner |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
HYPROP
lcsh:Hydraulic engineering Soil test Mean squared error Soil texture soil water retention curve Geography Planning and Development 0207 environmental engineering Soil science 02 engineering and technology Aquatic Science Biochemistry lcsh:Water supply for domestic and industrial purposes Pedotransfer function lcsh:TC1-978 medicine Organic matter international soils 020701 environmental engineering evaporation method Water Science and Technology chemistry.chemical_classification lcsh:TD201-500 04 agricultural and veterinary sciences Bulk density Water retention chemistry Soil water 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science medicine.symptom artificial neural networks |
| Zdroj: | Water, Vol 12, Iss 3425, p 3425 (2020) Water Volume 12 Issue 12 |
| ISSN: | 2073-4441 |
| Popis: | Direct measurements of soil hydraulic properties are time-consuming, challenging, and often expensive. Therefore, their indirect estimation via pedotransfer functions (PTFs) based on easily collected properties like soil texture, bulk density, and organic matter content is desirable. This study was carried out to assess the accuracy of the pseudo continuous neural network PTF (PCNN-PTF) approach for estimating the soil water retention curve of 153 international soils (a total of 12,654 measured water retention pairs) measured via the evaporation method. In addition, an independent data set from Turkey (79 soil samples with 7729 measured data pairs) was used to evaluate the reliability of the PCNN-PTF. The best PCNN-PTF showed high accuracy (root mean square error (RMSE) = 0.043 cm3 cm&minus 3) and reliability (RMSE = 0.061 cm3 cm&minus 3). When Turkish soil samples were incorporated into the training data set, the performance of the PCNN-PTF was enhanced by 33%. Therefore, to further improve the performance of the PCNN-PTF for new regions, we recommend the incorporation of local soils, when available, into the international data sets and developing new sets of PCNN-PTFs. |
| Databáze: | OpenAIRE |
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