A pseudo-continuous neural network approach for developing water retention pedotransfer functions with limited data

By Amir Haghverdi, Wim Cornelis, B. Ghahraman. 2012

78 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/87181bd4503dde8c9b85fd5eb15c7046c333fe59

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Wim Cornelis
2. Amir Haghverdi
3. H. Öztürk
4. Attila Nemes
5. E. Van Ranst
6. P. Mafuka
7. Y. Botula
8. M. G. Schaap

In Management, Monitoring, Policy and Law

1. H. Öztürk
2. Wim Cornelis
3. Amir Haghverdi

Citations in the corpus

• Revisiting the pseudo continuous pedotransfer function concept: impact of data quality and data mining method (2014, methodology): “, WRC fitting and PTF derivation, the soil hydraulic equation could be withdrawn and replaced by an appropriate neural network (NN) model (Haghverdi et al., 2012; Jain et al., 2004).”
• Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametric k‐Nearest Neighbor Approach (2013, methodology): “From Table 3 and from previous observations made by several researchers such as Schaap et al. (2001), Vereecken et al. (2010), and Haghverdi et al. (2012), there seems to be an ef ect of the combinations of dif erent input variables on the quality of the prediction of water contents at various…”