Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions Article

Khosravi, Khabat, Ngo, Phuong TT, Barzegar, Rahim et al. (2022). Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions . 32(5), 718-732. 10.1016/j.pedsph.2022.06.009

International Collaboration

cited authors

  • Khosravi, Khabat; Ngo, Phuong TT; Barzegar, Rahim; Quilty, John; Aalami, Mohammad T; Bui, Dieu T

sustainable development goals

authors

publication date

  • August 24, 2022

webpage

keywords

  • ADDITIVE REGRESSION
  • Agriculture
  • ENSEMBLE
  • FLOW
  • IMPLEMENTATION
  • Life Sciences & Biomedicine
  • PERFORMANCE
  • RANDOM FOREST
  • SEQUENTIAL MINIMAL OPTIMIZATION
  • SVM
  • Science & Technology
  • Soil Science
  • TREES
  • WATER
  • additive regression
  • empirical model
  • hybrid algorithms
  • soil water infiltration
  • weighted instances handler wrapper

Digital Object Identifier (DOI)

publisher

  • SCIENCE PRESS

start page

  • 718

end page

  • 732

volume

  • 32

issue

  • 5