干旱区生态环境知识资源中心(Arid): Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border)

Arid

DOI	10.1007/s11356-022-23982-x
	Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border)
	Pourhashemi, Sima; Asadi, Mohammad Ali Zangane; Boroughani, Mahdi; Azadi, Hossein
通讯作者	Asadi, MAZ
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2023
卷号	30 期号:10 页码:27965-27979
英文摘要	A dust storm is a major environmental problem affecting many arid regions worldwide. The novel contribution of this study is combining indicators extracted from RS- and statistic-based predictive models to spatial mapping of land susceptibility to dust emissions in a very important dust source area in the borders of Iran and Iraq (Khuzestan province in Iran and Al-Basrah and Maysan provinces in Iraq). In this research, remote sensing (RS) techniques and machine learning techniques, including multivariate adaptive regression spline (MARS), random forest (RF), and logistic regression (LR), were used for dust source identification and susceptibility map preparation. To this end, 152 DSA for the period of 2005-2020 were identified in the study area. Of these DSA data, 70% was assigned to the Dust Source Susceptibility Mapping (DSSM) (training dataset) and 30% to model validation. Consequently, six factors (i.e., soil, lithology, slope, normalized vegetation differential index (NDVI), geomorphology, and land use units) were prepared as DSA's independent and effective variables. The results of all three models indicated that land use had the most impact on DSA. The validation results of these models using the test data showed sub-curves of 0.92, 0.86, and 0.76 for the RF, MARS, and LR models, respectively. Also, results showed that the RF model outperformed MARS (AUC = 0.89) and LR (AUC = 0.78) methods. In all three models, high and very high susceptibility classes generally covered a large percentage of the case study. The highest percentage of dust source points was also in this susceptibility category. Overall, the results of this study can be useful for planners and managers to control and reduce the risk of negative dust consequences.
英文关键词	Dust storm Multivariate adaptive regression spline (MARS) Random forest (RF) Logistic regression (LR)
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000884955500005
WOS关键词	RANDOM-FOREST ; STORM ; REGRESSION ; CLIMATE ; COVER ; GIS ; IDENTIFICATION ; KHUZESTAN ; FREQUENCY ; EMISSION
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/2XILL650/396248
推荐引用方式 GB/T 7714	Pourhashemi, Sima,Asadi, Mohammad Ali Zangane,Boroughani, Mahdi,et al. Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border)[J],2023,30(10):27965-27979.
APA	Pourhashemi, Sima,Asadi, Mohammad Ali Zangane,Boroughani, Mahdi,&Azadi, Hossein.(2023).Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border).ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,30(10),27965-27979.
MLA	Pourhashemi, Sima,et al."Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border)".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 30.10(2023):27965-27979.