Romulus Costache - Search Results

Year	Number of Results
2018	1
2019	5
2020	4
2021	3
2022	1

1

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors.

Costache R, Arabameri A, Blaschke T, Pham QB, Pham BT, Pandey M, Arora A, Linh NTT, Costache I. Costache R, et al. Sensors (Basel). 2021 Jan 4;21(1):280. doi: 10.3390/s21010280. Sensors (Basel). 2021. PMID: 33406613 Free PMC article.

2

Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree.

Wang Y, Fang Z, Hong H, Costache R, Tang X. Wang Y, et al. Among authors: costache r. J Environ Manage. 2021 Jul 1;289:112449. doi: 10.1016/j.jenvman.2021.112449. Epub 2021 Mar 31. J Environ Manage. 2021. PMID: 33812150

3

Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models.

Ke B, Nguyen H, Bui XN, Bui HB, Choi Y, Zhou J, Moayedi H, Costache R, Nguyen-Trang T. Ke B, et al. Among authors: costache r. Chemosphere. 2021 Aug;276:130204. doi: 10.1016/j.chemosphere.2021.130204. Epub 2021 Mar 9. Chemosphere. 2021. PMID: 34088091

4

Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment.

Costache R, Pham QB, Avand M, Thuy Linh NT, Vojtek M, Vojteková J, Lee S, Khoi DN, Thao Nhi PT, Dung TD. Costache R, et al. J Environ Manage. 2020 Jul 1;265:110485. doi: 10.1016/j.jenvman.2020.110485. Epub 2020 Apr 20. J Environ Manage. 2020. PMID: 32421551

5

Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles.

Costache R, Tien Bui D. Costache R, et al. Sci Total Environ. 2020 Apr 10;712:136492. doi: 10.1016/j.scitotenv.2019.136492. Epub 2020 Jan 7. Sci Total Environ. 2020. PMID: 31927448

6

Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models.

Costache R, Hong H, Pham QB. Costache R, et al. Sci Total Environ. 2020 Apr 1;711:134514. doi: 10.1016/j.scitotenv.2019.134514. Epub 2019 Oct 8. Sci Total Environ. 2020. PMID: 31812401

7

A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area.

Tien Bui D, Hoang ND, Martínez-Álvarez F, Ngo PT, Hoa PV, Pham TD, Samui P, Costache R. Tien Bui D, et al. Among authors: costache r. Sci Total Environ. 2020 Jan 20;701:134413. doi: 10.1016/j.scitotenv.2019.134413. Epub 2019 Sep 12. Sci Total Environ. 2020. PMID: 31706212

8

Flood susceptibility evaluation through deep learning optimizer ensembles and GIS techniques.

Costache R, Arabameri A, Costache I, Crăciun A, Md Towfiqul Islam AR, Abba SI, Sahana M, Pham BT. Costache R, et al. J Environ Manage. 2022 Aug 15;316:115316. doi: 10.1016/j.jenvman.2022.115316. Epub 2022 May 19. J Environ Manage. 2022. PMID: 35598454

9

Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm.

Wang Y, Hong H, Chen W, Li S, Panahi M, Khosravi K, Shirzadi A, Shahabi H, Panahi S, Costache R. Wang Y, et al. Among authors: costache r. J Environ Manage. 2019 Oct 1;247:712-729. doi: 10.1016/j.jenvman.2019.06.102. Epub 2019 Jul 4. J Environ Manage. 2019. PMID: 31279803

10

Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania.

Costache R, Tien Bui D. Costache R, et al. Sci Total Environ. 2019 Nov 15;691:1098-1118. doi: 10.1016/j.scitotenv.2019.07.197. Epub 2019 Jul 16. Sci Total Environ. 2019. PMID: 31466192

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