Artificial neural networks for predicting soil water retention data of various Brazilian soils
Author:
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01115-3.pdf
Reference56 articles.
1. Al Majou H, Hassani B, Bruand A (2018) Transferability of continuous- and class-pedotransfer functions to predict water retention properties of semiarid syrian soils. Soil Use Manag 34:354–369. https://doi.org/10.1111/sum.12424
2. Angelaki A, Bota V, Chalkidis I (2023) Estimation of hydraulic parameters from the Soil. Water Characteristic Curve Sustainability 15:6714. https://doi.org/10.3390/su15086714
3. Barros AHC, de Jong Q (2014) Pedotransfer Functions for brazilian soils. Application of Soil Physics in environmental analyses. Springer International Publishing, Cham, pp 131–162. https://doi.org/10.1007/978-3-319-06013-2_6
4. Borgesen CD, Schaap MG (2005) Point and parameter pedotransfer functions for water retention predictions for danish soils. Geoderma 127:154–167. https://doi.org/10.1016/j.geoderma.2004.11.025
5. Botula YD, Cornelis WM, Baert G, Van Ranst E, Congo DR (2012) Agric Water Manag 111:1–10. https://doi.org/10.1016/j.agwat.2012.04.006
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