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Hybridized neural fuzzy ensembles for dust source modeling and prediction

Rahmati O. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam|
Tien Bui D. Department of Computer Engineering, University of Qazvin, Qazvin, Iran| Mohtashamian M. Department of Watershed Management, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Iran| Moghaddam D.D. Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran| Khosravi H. Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran| Shirzadi A. Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran| Shahabi H. Department of Natural Environment and Biodiversity, College of Environment, Karaj, Iran| Kornejady A. Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209, Neungdong-ro, Gwangin-gu, Seoul, 05006, South Korea| Goshtasb H. Center for Advanced Modeling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology SydneyNSW 2007, Australia| Jahani A. Department of Geography, Texas State University, San Marcos, TX 78666, United States| Pradhan B. 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| Tiefenbacher J.P. Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran| Deo R.C. Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon, 34132, South Korea| Ghiasi S.S. Division of Science Education, Kangwon National University, Chuncheon-si, Gangwon-do 24341, South Korea| Panahi M. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam|

Atmospheric Environment Số , năm 2020 (Tập 224, trang -)

DOI: 10.1016/j.atmosenv.2020.117320

Tài liệu thuộc danh mục: ISI, Scopus

English

English

Từ khóa: 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
Tóm tắt tiếng anh
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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