Arabameri A - Search Results

1

A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility.

Arabameri A, Saha S, Roy J, Tiefenbacher JP, Cerda A, Biggs T, Pradhan B, Thi Ngo PT, Collins AL. Arabameri A, et al. Sci Total Environ. 2020 Jul 15;726:138595. doi: 10.1016/j.scitotenv.2020.138595. Epub 2020 Apr 12. Sci Total Environ. 2020. PMID: 32320885

2

GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches.

Arabameri A, Rezaei K, Cerda A, Lombardo L, Rodrigo-Comino J. Arabameri A, et al. Sci Total Environ. 2019 Mar 25;658:160-177. doi: 10.1016/j.scitotenv.2018.12.115. Epub 2018 Dec 11. Sci Total Environ. 2019. PMID: 30577015

3

A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran.

Arabameri A, Rezaei K, Cerdà A, Conoscenti C, Kalantari Z. Arabameri A, et al. Sci Total Environ. 2019 Apr 10;660:443-458. doi: 10.1016/j.scitotenv.2019.01.021. Epub 2019 Jan 5. Sci Total Environ. 2019. PMID: 30640112

4

Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility.

Arabameri A, Yamani M, Pradhan B, Melesse A, Shirani K, Tien Bui D. Arabameri A, et al. Sci Total Environ. 2019 Oct 20;688:903-916. doi: 10.1016/j.scitotenv.2019.06.205. Epub 2019 Jun 20. Sci Total Environ. 2019. PMID: 31255826

5

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study.

Arabameri A, Blaschke T, Pradhan B, Pourghasemi HR, Tiefenbacher JP, Bui DT. Arabameri A, et al. Sensors (Basel). 2020 Jan 7;20(2):335. doi: 10.3390/s20020335. Sensors (Basel). 2020. PMID: 31936038 Free PMC article.

6

Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India.

Saha S, Roy J, Arabameri A, Blaschke T, Tien Bui D. Saha S, et al. Among authors: arabameri a. Sensors (Basel). 2020 Feb 28;20(5):1313. doi: 10.3390/s20051313. Sensors (Basel). 2020. PMID: 32121238 Free PMC article.

7

Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India.

Saha S, Saha M, Mukherjee K, Arabameri A, Ngo PTT, Paul GC. Saha S, et al. Among authors: arabameri a. Sci Total Environ. 2020 Aug 15;730:139197. doi: 10.1016/j.scitotenv.2020.139197. Epub 2020 May 4. Sci Total Environ. 2020. PMID: 32402979

8

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India.

Arora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT, Mishra VN, Bhardwaj A. Arora A, et al. Among authors: arabameri a. Sci Total Environ. 2021 Jan 1;750:141565. doi: 10.1016/j.scitotenv.2020.141565. Epub 2020 Aug 13. Sci Total Environ. 2021. PMID: 32882492

9

Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method.

Saha S, Arabameri A, Saha A, Blaschke T, Ngo PTT, Nhu VH, Band SS. Saha S, et al. Among authors: arabameri a. Sci Total Environ. 2021 Apr 10;764:142928. doi: 10.1016/j.scitotenv.2020.142928. Epub 2020 Oct 20. Sci Total Environ. 2021. PMID: 33127137

10

Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS.

Arabameri A, Pradhan B, Rezaei K. Arabameri A, et al. J Environ Manage. 2019 Feb 15;232:928-942. doi: 10.1016/j.jenvman.2018.11.110. Epub 2018 Dec 10. J Environ Manage. 2019. PMID: 33395761

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