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Page 1
Land subsidence modelling using tree-based machine learning algorithms.
Sci Total Environ. 2019 Jul 1;672:239-252. doi: 10.1016/j.scitotenv.2019.03.496. Epub 2019 Apr 2.
Sci Total Environ. 2019.
PMID: 30959291
Development of novel hybridized models for urban flood susceptibility mapping.
Rahmati O, Darabi H, Panahi M, Kalantari Z, Naghibi SA, Ferreira CSS, Kornejady A, Karimidastenaei Z, Mohammadi F, Stefanidis S, Tien Bui D, Haghighi AT.
Rahmati O, et al. Among authors: naghibi sa.
Sci Rep. 2020 Jul 31;10(1):12937. doi: 10.1038/s41598-020-69703-7.
Sci Rep. 2020.
PMID: 32737384
Free PMC article.
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Human-induced arsenic pollution modeling in surface waters - An integrated approach using machine learning algorithms and environmental factors.
Mohammadi M, Naghibi SA, Motevalli A, Hashemi H.
Mohammadi M, et al. Among authors: naghibi sa.
J Environ Manage. 2022 Mar 1;305:114347. doi: 10.1016/j.jenvman.2021.114347. Epub 2021 Dec 24.
J Environ Manage. 2022.
PMID: 34954681
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Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia.
Rahmati O, Falah F, Dayal KS, Deo RC, Mohammadi F, Biggs T, Moghaddam DD, Naghibi SA, Bui DT.
Rahmati O, et al. Among authors: naghibi sa.
Sci Total Environ. 2020 Jan 10;699:134230. doi: 10.1016/j.scitotenv.2019.134230. Epub 2019 Sep 6.
Sci Total Environ. 2020.
PMID: 31522053
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Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential.
Naghibi SA, Dolatkordestani M, Rezaei A, Amouzegari P, Heravi MT, Kalantar B, Pradhan B.
Naghibi SA, et al.
Environ Monit Assess. 2019 Mar 27;191(4):248. doi: 10.1007/s10661-019-7362-y.
Environ Monit Assess. 2019.
PMID: 30919064
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Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS.
Golkarian A, Naghibi SA, Kalantar B, Pradhan B.
Golkarian A, et al. Among authors: naghibi sa.
Environ Monit Assess. 2018 Feb 17;190(3):149. doi: 10.1007/s10661-018-6507-8.
Environ Monit Assess. 2018.
PMID: 29455381
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GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.
Naghibi SA, Pourghasemi HR, Dixon B.
Naghibi SA, et al.
Environ Monit Assess. 2016 Jan;188(1):44. doi: 10.1007/s10661-015-5049-6. Epub 2015 Dec 19.
Environ Monit Assess. 2016.
PMID: 26687087
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