Hybridized neural fuzzy ensembles for dust source modeling and prediction

Rahmati, Omid and Panahi, Mahdi and Ghiasi, Seid Saeid and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Tiefenbacher, John P. and Pradhan, Biswajeet and Jahani, Ali and Goshtasb, Hamid and Kornejady, Aiding and Shahabi, Himan and Shirzadi, Ataollah and Khosravi, Hassan and Moghaddam, Davoud Davoudi and Mohtashamian, Maryamsadat and Bui, Dieu Tien (2020) Hybridized neural fuzzy ensembles for dust source modeling and prediction. Atmospheric Environment, 224:117320. pp. 1-11. ISSN 1352-2310


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.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Historic - Institute for Resilient Regions - Centre for Health, Informatics and Economic Research (1 Aug 2018 - 31 Mar 2020)
Date Deposited: 26 Feb 2020 02:50
Last Modified: 02 Jun 2021 04:51
Uncontrolled Keywords: environmental modeling; dust; neural fuzzy; ensemble; Iran
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460510 Recommender systems
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: https://doi.org/10.1016/j.atmosenv.2020.117320
URI: http://eprints.usq.edu.au/id/eprint/38322

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