globalchange  > 气候变化与战略
DOI: 10.1016/j.atmosenv.2020.117320
论文题名:
Hybridized neural fuzzy ensembles for dust source modeling and prediction
作者: 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.
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 224
语种: 英语
英文关键词: 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 ; ozone ; rain ; algorithm ; atmospheric modeling ; dust ; dust storm ; ensemble forecasting ; environmental modeling ; fuzzy mathematics ; machine learning ; pollutant source ; prediction ; air monitoring ; air temperature ; Article ; artificial neural network ; bat algorithm ; cost ; cultural algorithm ; desertification ; differential evolution algorithm ; dust ; evaporation ; fuzzy system ; hybridized neural fuzzy ; land use ; metaheuristics ; precipitation ; prediction ; priority journal ; process optimization ; receiver operating characteristic ; remote sensing ; wind erosion ; wind speed ; Iran
学科: Dust ; Ensemble ; Environmental modeling ; Iran ; Neural fuzzy
中文摘要: 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
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160924
Appears in Collections:气候变化与战略

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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 SydneyNSW 2007, Australia; Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209, Neungdong-ro, Gwangin-gu, Seoul, 05006, South Korea; Department of Natural Environment and Biodiversity, College of Environment, Karaj, Iran; Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran; Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran; Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran; Department of Watershed Management, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Iran; Department of Computer Engineering, University of Qazvin, Qazvin, Iran; Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam

Recommended Citation:
Rahmati O.,Panahi M.,Ghiasi S.S.,et al. Hybridized neural fuzzy ensembles for dust source modeling and prediction[J]. Atmospheric Environment,2020-01-01,224
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