Estimating Saturated Hydraulic Conductivity Using Genetic Programming

By Kamban Parasuraman, Amin Elshorbagy, Bingcheng Si. 2007

75 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/ad7d0503655721fdaaf1b11eca33e8114a52f80f

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. Attila Nemes
3. E. Van Ranst
4. P. Mafuka
5. Y. Botula
6. Sabine Grunwald
7. Chaopeng Shen
8. Budiman Minasny

In Environmental Engineering

1. Wim Cornelis
2. Attila Nemes
3. Sabine Grunwald
4. E. Van Ranst
5. Chaopeng Shen
6. P. Mafuka
7. Y. Botula
8. Jianwei Ma

In Soil Science

1. Attila Nemes
2. Sabine Grunwald
3. Chaopeng Shen
4. Wim Cornelis
5. Budiman Minasny

Citations in the corpus

• Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametric k‐Nearest Neighbor Approach (2013, methodology): “Parasuraman et al. (2007) indicated that adoption of the ensemble technique in the formulation of PTFs helps in addressing one of the pertinent issues in any machine learning algorithm, namely generalization of the estimation results.”
• A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, background): “Another similar study found that Ks is larger when there is a higher sand percentage, but bulk density has a negative impact (Parasuraman et al., 2007).”
• Multi-criteria characterization of recent digital soil mapping and modeling approaches (2009, methodology): “DSMM studies that showed a high Know-Q rank (≥3) used complex pedo-transfer functions based on neural networks and/or genetic programming (Parasuraman et al., 2008), Monte Carlo Simulation (MCS) and Unconditioned Simulation of a Gaussian Random Field (USGRF) and window-based local estimation of…”