Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran Article

Rahmati, Omid, Pourghasemi, Hamid Reza, Melesse, Assefa M. (2016). Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran . CATENA, 137 360-372. 10.1016/j.catena.2015.10.010

ESI Most Cited Paper International Collaboration

cited authors

  • Rahmati, Omid; Pourghasemi, Hamid Reza; Melesse, Assefa M

sustainable development goals

authors

publication date

  • February 1, 2016

published in

keywords

  • Agriculture
  • DECISION-ANALYSIS
  • ENSEMBLE BIVARIATE
  • EVIDENTIAL BELIEF FUNCTION
  • FREQUENCY RATIO
  • GEOGRAPHICAL INFORMATION-SYSTEM
  • GIS
  • Geology
  • Geosciences, Multidisciplinary
  • Groundwater potential
  • Iran
  • LOGISTIC-REGRESSION
  • Life Sciences & Biomedicine
  • Maximum entropy (ME)
  • Mehran Region
  • Physical Sciences
  • Random forest (RF)
  • SENSITIVITY ANALYSIS
  • SPATIAL PREDICTION
  • SPECIES DISTRIBUTIONS
  • Science & Technology
  • Soil Science
  • UNCERTAINTY ANALYSIS
  • Water Resources

Digital Object Identifier (DOI)

publisher

  • ELSEVIER

start page

  • 360

end page

  • 372

volume

  • 137