Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations
Author:
Affiliation:
1. Life and Environmental Sciences DepartmentUniversity of California Merced CA USA
Funder
National Science Foundation
Publisher
American Geophysical Union (AGU)
Subject
Water Science and Technology
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR024357
Reference105 articles.
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2. Spatio-temporal variability in physical properties of different textured soils under similar management and semi-arid climatic conditions
3. A review of the changes in the soil pore system due to soil deformation: A hydrodynamic perspective
4. Selection bias in gene extraction on the basis of microarray gene-expression data
5. Estimation of models for cumulative infiltration of soil using machine learning methods
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