Database extension for digital soil mapping using artificial neural networks-Reference-Cited by-同舟云学术

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Database extension for digital soil mapping using artificial neural networks

Published:2016-11 Issue:18 Volume:9 Page:
ISSN:1866-7511
Container-title:Arabian Journal of Geosciences
language:en
Short-container-title:Arab J Geosci

Author:

Bagheri Bodaghabadi Mohsen,Martínez-Casasnovas José A.,Esfandiarpour Borujeni I.,Salehi M. H.,Mohammadi J.,Toomanian N.

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

http://link.springer.com/content/pdf/10.1007/s12517-016-2732-z.pdf

Reference39 articles.

1. Abdollahzadeh A, Mukhlisin M, Shafie A (2011) Predict soil erosion with artificial neural network in Tanakami (Japan). WSEAS Trans Comput 10:51–60

2. Amini M, Abbaspou KC, Khadem H, Fathianpour N, Afyuni M, Schulin R (2005) Neural network models to predict cation exchange capacity in arid regions of Iran. Eur J Soil Sci 56(4):551–559

3. Bagheri Bodaghabadi M, Salehi MH, Martínez-Casasnovas JA, Mohammadi J, Toomanian N, Esfandiarpoor Borujeni I (2011) Using canonical correspondence analysis (CCA) to identify the most important DEM attributes for digital soil mapping applications. Catena 86:66–74

4. Bagheri Bodaghabadi M, Martínez-Casasnovas JA, Salehi MH, Mohammadi J, Toomanian N, Esfandiarpoor Borujeni I (2015a) Digital soil mapping using artificial neural network (ANN) and digital elevation model (DEM) attributes. Pedosphere 25:580–591

5. Bagheri Bodaghabadi M, Martínez-Casasnovas JA, Khalili P, Masihabadi M (2015b) Assessment of the FAO traditional land evaluation methods, a case study: Iranian land classification method. Soil and Use Management 31:384–396

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