干旱区生态环境知识资源中心(Arid): Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran

Arid

DOI	10.1016/j.aeolia.2021.100682
	Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran
	Gholami, Hamid; Mohammadifar, Aliakbar; Golzari, Shahram; 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	AEOLIAN RESEARCH
ISSN	1875-9637
EISSN	2212-1684
出版年	2021
卷号	50
英文摘要	Wind erosion have many negative effects on global terrestrial and aquatic ecosystems and these phenomena are controlled by several factors including climatic, meteorological, topographic, vegetation, surface and soil characteristics. This study applied, for the first time, the Boruta algorithm for identification of effective variables controlling wind erosion. The novelty of the study was increased further using application of two deep learning (DL) models comprising a simple recurrent neural network (RNN) and restricted boltzmann machine (RBM). Collectively, these tools were used to map land susceptibility to wind erosion in parts of Kerman province, southeastern Iran. Among 18 potential variables for controlling dust emissions via wind erosion, 4 and 14 were identified as non-important and important, respectively, by the Boruta algorithm, while three (precipitation, digital elevation model and soil organic carbon) were selected as the most important factors. An inventory map of the wind erosion confirmed using both a test dataset (30%) and a training dataset (70%) was used to construct predictive models of land susceptibility to wind erosion. Both DL predictive models exhibited highly satisfactory performance according to a Taylor diagram, but the simple RNN performed slightly better than RBM. Based on the simple RNN, 35.6%, 5%, 2.4%, 22.7% and 34.3% of the total study area were characterized by very low, low, moderate, high and very high susceptibility, respectively. Convergent prediction of the same susceptibility classes by intersecting the maps generated by both models classified 17.4%, 0.07%, 0.06%, 7.4% and 34% of the total study area as very low, low, moderate, high and very high susceptibility classes, respectively. We conclude that applying the Boruta algorithm and DL models as new methods in aeolian geomorphology, may provide accurate spatial maps of dust sources to help target mitigation of detrimental dust effects on climate, ecosystems and human health.
英文关键词	Wind erosion Land susceptibility Convergent prediction Taylor diagram Recurrent neural network Iran
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000647796000001
WOS关键词	NEURAL-NETWORKS ; WRF-CHEM ; JAZMURIAN BASIN ; SOIL-EROSION ; MIDDLE-EAST ; DESERT DUST ; WIND ; STORMS ; ASIA ; SEA
WOS类目	Geography, Physical
WOS研究方向	Physical Geography
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/349300
作者单位	[Gholami, Hamid; Mohammadifar, Aliakbar] Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran; [Golzari, Shahram] Univ Hormozgan, Dept Elect & Comp Engn, Bandar Abbas, Hormozgan, Iran; [Golzari, Shahram] Univ Hormozgan, Deep Learning Res Grp, Bandar Abbas, Hormozgan, 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,Mohammadifar, Aliakbar,Golzari, Shahram,et al. Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran[J],2021,50.
APA	Gholami, Hamid,Mohammadifar, Aliakbar,Golzari, Shahram,Kaskaoutis, Dimitris G.,&Collins, Adrian L..(2021).Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran.AEOLIAN RESEARCH,50.
MLA	Gholami, Hamid,et al."Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran".AEOLIAN RESEARCH 50(2021).