干旱区生态环境知识资源中心(Arid): Hybridized neural fuzzy ensembles for dust source modeling and prediction

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DOI	10.1016/j.atmosenv.2020.117320
	Hybridized neural fuzzy ensembles for dust source modeling and prediction
	Rahmati, Omid 1,2; Panahi, Mahdi 3,4; Ghiasi, Seid Saeid 5; Deo, Ravinesh C.6,7; Tiefenbacher, John P.8; Pradhan, Biswajeet 9,10; Jahani, Ali 11; Goshtasb, Hamid 11; Kornejady, Aiding 12; Shahabi, Himan 13,14; Shirzadi, Ataollah 13; Khosravi, Hassan 5; Moghaddam, Davoud Davoudi 15; Mohtashamian, Maryamsadat 16; Dieu Tien Bui 17
通讯作者	Dieu Tien Bui
来源期刊	ATMOSPHERIC ENVIRONMENT
ISSN	1352-2310
EISSN	1873-2844
出版年	2020
卷号	224
英文摘要	Dust storms are believed to play an essential role in many climatological, geochemical, and environmental processes. This atmospheric phenomenon can have a significant negative impact on public health and significantly disturb natural ecosystems. Identifying dust-source areas is thus a fundamental task to control the effects of this hazard. This study is the first attempt to identify dust source areas using hybridized machine-learning algorithms. Each hybridized model, designed as an intelligent system, consists of an adaptive neuro-fuzzy inference system (ANFIS), integrated with a combination of metaheuristic optimization algorithms: the bat algorithm (BA), cultural algorithm (CA), and differential evolution (DE). The data acquired from two key sources - the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue and the Ozone Monitoring Instrument (OMI) - are incorporated into the hybridized model, along with relevant data from field surveys and dust samples. Goodness-of-fit analyses are performed to evaluate the predictive capability of the hybridized models using different statistical criteria, including the true skill statistic (TSS) and the area under the receiver operating characteristic curve (AUC). The results demonstrate that the hybridized ANFIS-DE model (with AUC = 84.1%, TSS = 0.73) outperforms the other comparative hybridized models tailored for dust-storm prediction. The results provide evidence that the hybridized ANFIS-DE model should be explored as a promising, cost-effective method for efficiently identifying the dust-source areas, with benefits for both public health and natural environments where excessive dust presents significant challenges.
英文关键词	Environmental modeling Dust Neural fuzzy Ensemble Iran
类型	Article
语种	英语
国家	Vietnam ; South Korea ; Iran ; Australia ; USA
收录类别	SCI-E
WOS记录号	WOS:000525865300032
WOS关键词	DIFFERENTIAL EVOLUTION ; INFERENCE SYSTEM ; DESERT DUST ; SOURCE REGIONS ; ANFIS ; IMPACT ; STORMS ; ANN ; OPTIMIZATION ; ALGORITHM
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314150
作者单位	1.Ton Duc Thang Univ, Geog Informat Sci Res Grp, Ho Chi Minh City, Vietnam; 2.Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam; 3.Kangwon Natl Univ, Div Sci Educ, Chuncheon Si 24341, Gangwon Do, South Korea; 4.Korea Inst Geosci & Mineral Resources KIGAM, Geosci Platform Res Div, 124 Gwahak Ro, Daejeon 34132, South Korea; 5.Univ Tehran, Fac Nat Resources, Dept Arid & Mt Reg Reclamat, Karaj, Iran; 6.Univ Southern Queensland, Sch Agr Computat & Environm Sci, Ctr Sustainable Agr Syst, Springfield, Qld 4300, Australia; 7.Univ Southern Queensland, Ctr Appl Climate Sci, Springfield, Qld 4300, Australia; 8.Texas State Univ, Dept Geog, San Marcos, TX 78666 USA; 9.Univ Technol Sydney, Fac Engn & IT, CAMGIS, Sydney, NSW 2007, Australia; 10.Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea; 11.Coll Environm, Dept Nat Environm & Biodivers, Karaj, Iran; 12.Gorgan Univ Agr Sci & Nat Resources, Dept Watershed Management, Gorgan, Golestan, Iran; 13.Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj, Iran; 14.Univ Kurdistan, Kurdistan Studies Inst, Dept Zrebar Lake Environm Res, Sanandaj, Iran; 15.Lorestan Univ, Fac Agr & Nat Resources, Dept Watershed Management, Khorramabad, Iran; 16.Univ Qazvin, Dept Comp Engn, Qazvin, Iran; 17.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
推荐引用方式 GB/T 7714	Rahmati, Omid,Panahi, Mahdi,Ghiasi, Seid Saeid,et al. Hybridized neural fuzzy ensembles for dust source modeling and prediction[J],2020,224.
APA	Rahmati, Omid.,Panahi, Mahdi.,Ghiasi, Seid Saeid.,Deo, Ravinesh C..,Tiefenbacher, John P..,...&Dieu Tien Bui.(2020).Hybridized neural fuzzy ensembles for dust source modeling and prediction.ATMOSPHERIC ENVIRONMENT,224.
MLA	Rahmati, Omid,et al."Hybridized neural fuzzy ensembles for dust source modeling and prediction".ATMOSPHERIC ENVIRONMENT 224(2020).