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Items: 14

1.

Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia.

Rahmati O, Panahi M, Kalantari Z, Soltani E, Falah F, Dayal KS, Mohammadi F, Deo RC, Tiefenbacher J, Tien Bui D.

Sci Total Environ. 2019 Nov 25:134656. doi: 10.1016/j.scitotenv.2019.134656. [Epub ahead of print]

PMID:
31839310
2.

Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithms.

Karimidastenaei Z, Torabi Haghighi A, Rahmati O, Rasouli K, Rozbeh S, Pirnia A, Pradhan B, Kløve B.

Sci Total Environ. 2020 Mar 15;708:135115. doi: 10.1016/j.scitotenv.2019.135115. Epub 2019 Nov 20.

PMID:
31787309
3.

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.

Sci Total Environ. 2020 Jan 10;699:134230. doi: 10.1016/j.scitotenv.2019.134230. Epub 2019 Sep 6.

PMID:
31522053
4.

Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods.

Rahmati O, Choubin B, Fathabadi A, Coulon F, Soltani E, Shahabi H, Mollaefar E, Tiefenbacher J, Cipullo S, Ahmad BB, Tien Bui D.

Sci Total Environ. 2019 Oct 20;688:855-866. doi: 10.1016/j.scitotenv.2019.06.320. Epub 2019 Jun 21.

PMID:
31255823
5.

Land subsidence modelling using tree-based machine learning algorithms.

Rahmati O, Falah F, Naghibi SA, Biggs T, Soltani M, Deo RC, Cerdà A, Mohammadi F, Tien Bui D.

Sci Total Environ. 2019 Jul 1;672:239-252. doi: 10.1016/j.scitotenv.2019.03.496. Epub 2019 Apr 2.

PMID:
30959291
6.

Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities.

Rahmati O, Golkarian A, Biggs T, Keesstra S, Mohammadi F, Daliakopoulos IN.

J Environ Manage. 2019 Apr 15;236:466-480. doi: 10.1016/j.jenvman.2019.02.020. Epub 2019 Feb 13.

PMID:
30771667
7.

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination.

Sajedi-Hosseini F, Malekian A, Choubin B, Rahmati O, Cipullo S, Coulon F, Pradhan B.

Sci Total Environ. 2018 Dec 10;644:954-962. doi: 10.1016/j.scitotenv.2018.07.054. Epub 2018 Jul 11.

PMID:
30743892
8.

PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches.

Rahmati O, Kornejady A, Samadi M, Deo RC, Conoscenti C, Lombardo L, Dayal K, Taghizadeh-Mehrjardi R, Pourghasemi HR, Kumar S, Bui DT.

Sci Total Environ. 2019 May 10;664:296-311. doi: 10.1016/j.scitotenv.2019.02.017. Epub 2019 Feb 2.

PMID:
30743123
9.

Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models.

Azareh A, Rahmati O, Rafiei-Sardooi E, Sankey JB, Lee S, Shahabi H, Ahmad BB.

Sci Total Environ. 2019 Mar 10;655:684-696. doi: 10.1016/j.scitotenv.2018.11.235. Epub 2018 Nov 17.

PMID:
30476849
10.

Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria.

Alilou H, Rahmati O, Singh VP, Choubin B, Pradhan B, Keesstra S, Ghiasi SS, Sadeghi SH.

J Environ Manage. 2019 Feb 15;232:22-36. doi: 10.1016/j.jenvman.2018.11.019. Epub 2018 Nov 20.

PMID:
30466009
11.

How can statistical and artificial intelligence approaches predict piping erosion susceptibility?

Hosseinalizadeh M, Kariminejad N, Rahmati O, Keesstra S, Alinejad M, Mohammadian Behbahani A.

Sci Total Environ. 2019 Jan 1;646:1554-1566. doi: 10.1016/j.scitotenv.2018.07.396. Epub 2018 Jul 29.

PMID:
30235640
12.

River suspended sediment modelling using the CART model: A comparative study of machine learning techniques.

Choubin B, Darabi H, Rahmati O, Sajedi-Hosseini F, Kløve B.

Sci Total Environ. 2018 Feb 15;615:272-281. doi: 10.1016/j.scitotenv.2017.09.293. Epub 2017 Oct 2.

PMID:
28982076
13.

Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework.

Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Feizizadeh B.

Sci Total Environ. 2017 Feb 1;579:913-927. doi: 10.1016/j.scitotenv.2016.10.176. Epub 2016 Nov 23.

PMID:
27887837
14.

Application of Dempster-Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran.

Rahmati O, Melesse AM.

Sci Total Environ. 2016 Oct 15;568:1110-1123. doi: 10.1016/j.scitotenv.2016.06.176. Epub 2016 Jun 26.

PMID:
27358196

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