气候变化领域集成服务门户(CCPortal): Hybridized neural fuzzy ensembles for dust source modeling and prediction

CCPortal

DOI	10.1016/j.atmosenv.2020.117320
	Hybridized neural fuzzy ensembles for dust source modeling and prediction
	Rahmati O.; Panahi M.; Ghiasi S.S.; Deo R.C.; Tiefenbacher J.P.; Pradhan B.; Jahani A.; Goshtasb H.; Kornejady A.; Shahabi H.; Shirzadi A.; Khosravi H.; Moghaddam D.D.; Mohtashamian M.; Tien Bui D.
发表日期	2020
ISSN	13522310
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. © 2020
英文关键词	Dust; Ensemble; Environmental modeling; Iran; Neural fuzzy
学科领域	Cost effectiveness; Dust; Evolutionary algorithms; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Inference engines; Intelligent systems; Learning algorithms; Machine learning; Optimization; Public health; Radiometers; Storms; Supercomputers; Ultraviolet spectrometers; Adaptive neuro-fuzzy inference system; Ensemble; Environmental model; Iran; Meta-heuristic optimizations; Moderate resolution imaging spectroradiometer; Neural fuzzy; Receiver operating characteristic curves; Fuzzy inference; algorithm; atmospheric modeling; dust; dust storm; ensemble forecasting; environmental modeling; fuzzy mathematics; machine learning; pollutant source; prediction; Iran
语种	英语
scopus关键词	Cost effectiveness; Dust; Evolutionary algorithms; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Inference engines; Intelligent systems; Learning algorithms; Machine learning; Optimization; Public health; Radiometers; Storms; Supercomputers; Ultraviolet spectrometers; Adaptive neuro-fuzzy inference system; Ensemble; Environmental model; Iran; Meta-heuristic optimizations; Moderate resolution imaging spectroradiometer; Neural fuzzy; Receiver operating characteristic curves; Fuzzy inference; algorithm; atmospheric modeling; dust; dust storm; ensemble forecasting; environmental modeling; fuzzy mathematics; machine learning; pollutant source; prediction; Iran
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120732
作者单位	Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Division of Science Education, Kangwon National University, Chuncheon-si, Gangwon-do 24341, South Korea; Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon, 34132, South Korea; Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran; School of Agricultural, Computational and Environmental Sciences, Centre for Sustainable Agricultural Systems & Centre for Applied Climate Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia; Department of Geography, Texas State University, San Marcos, TX 78666, United States; Center for Advanced Modeling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Syd...
推荐引用方式 GB/T 7714	Rahmati O.,Panahi M.,Ghiasi S.S.,et al. Hybridized neural fuzzy ensembles for dust source modeling and prediction[J],2020,224.
APA	Rahmati O..,Panahi M..,Ghiasi S.S..,Deo R.C..,Tiefenbacher J.P..,...&Tien Bui D..(2020).Hybridized neural fuzzy ensembles for dust source modeling and prediction.Atmospheric Environment,224.
MLA	Rahmati O.,et al."Hybridized neural fuzzy ensembles for dust source modeling and prediction".Atmospheric Environment 224(2020).