干旱区生态环境知识资源中心(Arid): Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran

Arid

DOI	10.1016/j.gexplo.2021.106921
	Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran
	Azizi, Kamran; Ayoubi, Shamsollah; Nabiollahi, Kamal; Garosi, Younes; Gislum, Rene
通讯作者	Ayoubi, S
来源期刊	JOURNAL OF GEOCHEMICAL EXPLORATION
ISSN	0375-6742
EISSN	1879-1689
出版年	2022
卷号	233
英文摘要	The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe, Cu, Mn) in western Iran, using environmental covariates and applying two machine learning methods comprised Random forest (RF), and Cubist. In this respect, a combination of different input environmental variables (remote sensing data, topographic attributes, thematic maps and soil properties) were used in modeling under four scenarios (I: remote sensing data (RS); II: RS + topographic attributes resulted from digital elevation model (DEM); III: RS + topographic attributes + thematic maps; IV: RS + topographic attributes + thematic maps +soil properties). The maps of Euclidean distance from mines and roads as well as the geology map have been used as thematic maps. A total of 346 soil samples were taken using stratified random sampling from the surface layers (0-20 cm depth) of the studied area and selected heavy metals (Ni, Fe, Cu, Mn), and soil properties were measured in the laboratory. RF and Cubist models were used to predict soil heavy metals in four scenarios. The results indicated that the best prediction accuracy was achieved for the fourth scenario (IV) when all input variables were combined to predict selected heavy metals. Moreover, two models showed different capability for various metals. According to our results, the random forest model had a high accuracy in predicting Ni (R2 = 0.67) and Cu (R2 = 0.60), In contrast, the Cubist model had a higher accuracy in predicting Mn (R2 = 0.55). For predicting Fe, both models provided a similar accuracy (R2 = 0.73). This study proved the high capability of machine learning methods to use easily available environmental data to predict studied heavy metals in the large scale that are essential for decision making in sustainable management in agricultural and environmental concerns.
英文关键词	Digital soil mapping Topographic attributes Remote sensing Soil properties Pollution
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000793139400001
WOS关键词	SOIL ORGANIC-MATTER ; MAGNETIC-SUSCEPTIBILITY ; SPATIAL-DISTRIBUTION ; LAND-USE ; POLLUTION ASSESSMENT ; SEMIARID REGION ; TRACE-ELEMENTS ; ARID REGION ; CONTAMINATION ; GIS
WOS类目	Geochemistry & Geophysics
WOS研究方向	Geochemistry & Geophysics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393445
推荐引用方式 GB/T 7714	Azizi, Kamran,Ayoubi, Shamsollah,Nabiollahi, Kamal,et al. Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran[J],2022,233.
APA	Azizi, Kamran,Ayoubi, Shamsollah,Nabiollahi, Kamal,Garosi, Younes,&Gislum, Rene.(2022).Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran.JOURNAL OF GEOCHEMICAL EXPLORATION,233.
MLA	Azizi, Kamran,et al."Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran".JOURNAL OF GEOCHEMICAL EXPLORATION 233(2022).