Predicting land susceptibility to atmospheric dust emissions in central Iran by combining integrated data mining and a regional climate model

A - Papers appearing in refereed journals

Gholami, H., Mohamadifar, A., Rahimi, S., Kaskaoutis, D.G. and Collins, A. L. 2021. Predicting land susceptibility to atmospheric dust emissions in central Iran by combining integrated data mining and a regional climate model . Atmospheric Pollution Research. 12 (4), pp. 172-187. https://doi.org/10.1016/j.apr.2021.03.005

AuthorsGholami, H., Mohamadifar, A., Rahimi, S., Kaskaoutis, D.G. and Collins, A. L.
Abstract

This study aims to predict land susceptibility (a term that indicates the degree of sensitivity of land to detachment of soil particles by wind) to dust emissions in Yazd province, central Iran, by combining a new integrated data mining (DM) model and the RegCM4 climatic model. The study further determines the relative importance of key factors controlling dust emissions by applying 12 individual DM models. The integrated model is based on the individual models returning Nash Sutcliffe coefficient (NSC) values > 90% for the spatial modelling of land susceptibility to dust emissions and using the area under the curve (AUC) for validation. 13 key factors controlling dust emissions are mapped including soil characteristics, climatic variables, vegetation cover, a Digital Elevation Model (DEM), geology and land use. Based on Spearman clustering analysis and multi-collinearity tests (tolerance coefficient -TC and variance inflation factor -VIF), the effective factors for dust emissions are classified into nine clusters and no multi-collinearity is found among the effective factors. DEM, NDVI (normalized difference vegetation index), geology and calcium carbonate are identified as the most important factors controlling dust emissions. Seven individual models return NSC in the range of 90–98% and are used to generate the integrated model for the final mapping of land susceptibility to dust emissions. Among 851 pixels located in the dust sources, 30% (255 pixels) and 70% (596 pixels) are randomly selected as validation and training datasets, respectively for the new integrated model. Using this model, 9%, 17%, 7% and 67% of the study area correspond to low, moderate, high and very high susceptibility classes, while the validation results in AUC = 99.3%. Simulations with the RegCM4 model reveal high consistency regarding the spatial distribution of the most susceptible areas and dust emissions. Overall, combining DM approaches and physical models is useful in aeolian geomorphology studies.

KeywordsDust emissions ; Land-susceptibility factors; Data mining models; Land susceptibility mapping ; Iran
Year of Publication2021
JournalAtmospheric Pollution Research
Journal citation12 (4), pp. 172-187
Digital Object Identifier (DOI)https://doi.org/10.1016/j.apr.2021.03.005
Web address (URL)https://doi.org/10.1016/j.apr.2021.03.005
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
European Union
Funder project or codeS2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
“Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) Operational Program
Output statusPublished
Publication dates
Online13 Mar 2021
Publication process dates
Accepted09 Mar 2021
PublisherElsevier

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