Hybridized neural fuzzy ensembles for dust source modeling and prediction

Rahmati, O; Panahi, M; Ghiasi, SS; Deo, RC; Tiefenbacher, JP; Pradhan, B; Jahani, A; Goshtasb, H; Kornejady, A; Shahabi, H; Shirzadi, A; Khosravi, H; Moghaddam, DD; Mohtashamian, M; Tien Bui, D

Hybridized neural fuzzy ensembles for dust source modeling and prediction

Rahmati, O Panahi, M Ghiasi, SS Deo, RC Tiefenbacher, JP Pradhan, B

Jahani, A Goshtasb, H Kornejady, A Shahabi, H Shirzadi, A Khosravi, H Moghaddam, DD Mohtashamian, M Tien Bui, D

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Publication Type:: Journal Article
Citation:: Atmospheric Environment, 2020, 224
Issue Date:: 2020-03-01

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Field	Value	Language
dc.contributor.author	Rahmati, O	en_US
dc.contributor.author	Panahi, M	en_US
dc.contributor.author	Ghiasi, SS	en_US
dc.contributor.author	Deo, RC	en_US
dc.contributor.author	Tiefenbacher, JP	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Jahani, A	en_US
dc.contributor.author	Goshtasb, H	en_US
dc.contributor.author	Kornejady, A	en_US
dc.contributor.author	Shahabi, H	en_US
dc.contributor.author	Shirzadi, A	en_US
dc.contributor.author	Khosravi, H	en_US
dc.contributor.author	Moghaddam, DD	en_US
dc.contributor.author	Mohtashamian, M	en_US
dc.contributor.author	Tien Bui, D	en_US
dc.date.accessioned	2020-04-10T06:02:14Z
dc.date.available	2020-04-10T06:02:14Z
dc.date.issued	2020-03-01	en_US
dc.identifier.citation	Atmospheric Environment, 2020, 224	en_US
dc.identifier.issn	1352-2310	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139929
dc.description.abstract	© 2020 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.	en_US
dc.relation.ispartof	Atmospheric Environment	en_US
dc.relation.isbasedon	10.1016/j.atmosenv.2020.117320	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Meteorology & Atmospheric Sciences	en_US
dc.title	Hybridized neural fuzzy ensembles for dust source modeling and prediction	en_US
dc.type	Journal Article
utslib.citation.volume	224	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0401 Atmospheric Sciences	en_US
utslib.for	0907 Environmental Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-03-01T00:00:00+1100
pubs.publication-status	Published	en_US
pubs.volume	224	en_US

Abstract:

© 2020 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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http://hdl.handle.net/10453/139929