Title : ( An applied statistical method to identify desertification indicators in northeastern Iran )
Authors: Mehdi Sarparast , Majid Ownegh , Ali Najafinejad , Adel Sepehr ,Abstract
Background: Desertification could be considered ultimate consequence of land degradation in an ecosystem. Iran with more than 75% arid and semi-arid areas involves fragile and susceptible ecosystems to desertification. We applied a statistical algorithm including regression trees and random forest techniques for determining main factors affecting desertification based on ESAs in Taybad-Bakharz region at northeastern Iran. Results: The results indicated a significant correlation between the desertification hazard value with variables of wind erosion, precipitation, aridity index, technology development, slope index, vegetation state and land use changes. Conclusions: Regression trees and random forest techniques in desertification hazard provide an absolute estimation of the relationship between dependent and independent variables. We can use a robust base for further investigations and refined with findings from in-depth studies carried out at the local scale.
Keywords
, Desertification hazard, Regression trees, Random forest-, data mining@article{paperid:1067715,
author = { and and Ali Najafinejad and Sepehr, Adel},
title = {An applied statistical method to identify desertification indicators in northeastern Iran},
journal = {Geoenvironmental Disasters},
year = {2018},
volume = {5},
number = {3},
month = {March},
issn = {2197-8670},
pages = {1--10},
numpages = {9},
keywords = {Desertification hazard; Regression trees; Random forest-; data mining},
}
%0 Journal Article
%T An applied statistical method to identify desertification indicators in northeastern Iran
%A
%A
%A Ali Najafinejad
%A Sepehr, Adel
%J Geoenvironmental Disasters
%@ 2197-8670
%D 2018