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Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran

(2019) GEODERMA. 338. p.445-452
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Keywords
Environmental covariates, Soil characteristics, Spatial prediction, Modeling accuracy

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Citation

Please use this url to cite or link to this publication:

Chicago
Zeraatpisheh, Mojtaba, Shamsollah Ayoubi, Azam Jafari, Samaneh Tajik, and Peter Finke. 2019. “Digital Mapping of Soil Properties Using Multiple Machine Learning in a Semi-arid Region, Central Iran.” Geoderma 338: 445–452.
APA
Zeraatpisheh, M., Ayoubi, S., Jafari, A., Tajik, S., & Finke, P. (2019). Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran. GEODERMA, 338, 445–452.
Vancouver
1.
Zeraatpisheh M, Ayoubi S, Jafari A, Tajik S, Finke P. Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran. GEODERMA. 2019;338:445–52.
MLA
Zeraatpisheh, Mojtaba, Shamsollah Ayoubi, Azam Jafari, et al. “Digital Mapping of Soil Properties Using Multiple Machine Learning in a Semi-arid Region, Central Iran.” GEODERMA 338 (2019): 445–452. Print.
@article{8575610,
  author       = {Zeraatpisheh, Mojtaba and Ayoubi, Shamsollah and Jafari, Azam and Tajik, Samaneh and Finke, Peter},
  issn         = {0016-7061},
  journal      = {GEODERMA},
  language     = {eng},
  pages        = {445--452},
  title        = {Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran},
  url          = {http://dx.doi.org/10.1016/j.geoderma.2018.09.006},
  volume       = {338},
  year         = {2019},
}

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