Sunil Saha - Search Results

Year	Number of Results
2018	1
2019	1
2020	7
2021	1

1

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. Sensors (Basel). 2020 Feb 28;20(5):1313. doi: 10.3390/s20051313. Sensors (Basel). 2020. PMID: 32121238 Free PMC article.

2

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. Among authors: saha s. 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

3

Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms.

Gayen A, Pourghasemi HR, Saha S, Keesstra S, Bai S. Gayen A, et al. Among authors: saha s. Sci Total Environ. 2019 Jun 10;668:124-138. doi: 10.1016/j.scitotenv.2019.02.436. Epub 2019 Mar 1. Sci Total Environ. 2019. PMID: 30851678

4

Inadequate awareness of cervical cancer and its prevention among young women of Delhi, India: public health policy implications.

Sharma P, Bhilwar M, Tiwari P, Parija PP, Saha SK, Mehra K. Sharma P, et al. Among authors: saha sk. Int J Adolesc Med Health. 2020 May 1:/j/ijamh.ahead-of-print/ijamh-2019-0108/ijamh-2019-0108.xml. doi: 10.1515/ijamh-2019-0108. Online ahead of print. Int J Adolesc Med Health. 2020. PMID: 32352403

5

Examining the impact of lockdown (due to COVID-19) on ambient aerosols (PM_2.5): A study on Indo-Gangetic Plain (IGP) Cities, India.

Das M, Das A, Sarkar R, Saha S, Mandal A. Das M, et al. Among authors: saha s. Stoch Environ Res Risk Assess. 2020 Oct 16:1-17. doi: 10.1007/s00477-020-01905-x. Online ahead of print. Stoch Environ Res Risk Assess. 2020. PMID: 33100900 Free PMC article.

6

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. 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

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. 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

Use of information on pre-packaged foods among residents of an urban village of South Delhi, India.

Bhilwar M, Tiwari P, Saha SK, Sharma P, Parija PP. Bhilwar M, et al. Among authors: saha sk. Natl Med J India. 2018 Jul-Aug;31(4):211-214. doi: 10.4103/0970-258X.258219. Natl Med J India. 2018. PMID: 31134925 Free article.

9

Determinants of online health information-seeking behavior: A cross-sectional survey among residents of an urban settlement in Delhi.

Parija PP, Tiwari P, Sharma P, Saha SK. Parija PP, et al. Among authors: saha sk. J Educ Health Promot. 2020 Dec 29;9:344. doi: 10.4103/jehp.jehp_238_20. eCollection 2020. J Educ Health Promot. 2020. PMID: 33575380 Free PMC article.

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