干旱区生态环境知识资源中心(Arid): Predicting land susceptibility to atmospheric dust emissions in central Iran by combining integrated data mining and a regional climate model

Arid

DOI	10.1016/j.apr.2021.03.005
	Predicting land susceptibility to atmospheric dust emissions in central Iran by combining integrated data mining and a regional climate model
	Gholami, Hamid; Mohamadifar, Aliakbar; Rahimi, Setareh; Kaskaoutis, Dimitris G.; Collins, Adrian L.
通讯作者	Gholami, H (corresponding author), Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran.
来源期刊	ATMOSPHERIC POLLUTION RESEARCH
ISSN	1309-1042
出版年	2021
卷号	12 期号:4 页码:172-187
英文摘要	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.
英文关键词	Dust emissions Land-susceptibility factors Data mining models Land susceptibility mapping Iran
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000683374500004
WOS关键词	AEROSOL OPTICAL DEPTH ; MACHINE-LEARNING ALGORITHMS ; MIDDLE-EAST ; DESERT DUST ; GENETIC ALGORITHM ; PARTICULATE MATTER ; SPATIAL PREDICTION ; REGRESSION-MODEL ; JAZMURIAN BASIN ; STORMS
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/362663
作者单位	[Gholami, Hamid; Mohamadifar, Aliakbar] Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran; [Rahimi, Setareh] Univ Hormozgan, Fac Marine Sci & Technol, Bandar Abbas, Iran; [Kaskaoutis, Dimitris G.] Natl Observ Athens, Inst Environm Res & Sustainable Dev, Athens 15784, Greece; [Kaskaoutis, Dimitris G.] Univ Crete, Dept Chem, Environm Chem Proc Lab, Iraklion 71003, Greece; [Collins, Adrian L.] Rothamsted Res, Sustainable Agr Sci Dept, Okehampton EX20 2SB, Devon, England
推荐引用方式 GB/T 7714	Gholami, Hamid,Mohamadifar, Aliakbar,Rahimi, Setareh,et al. Predicting land susceptibility to atmospheric dust emissions in central Iran by combining integrated data mining and a regional climate model[J],2021,12(4):172-187.
APA	Gholami, Hamid,Mohamadifar, Aliakbar,Rahimi, Setareh,Kaskaoutis, Dimitris G.,&Collins, Adrian 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),172-187.
MLA	Gholami, Hamid,et al."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(2021):172-187.