Spatial modeling of radon potential mapping using deep learning algorithms-Reference-Cited by-同舟云学术

Spatial modeling of radon potential mapping using deep learning algorithms

Published:2022-01-05 Issue: Volume: Page:1-23
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Panahi Mahdi¹²^ORCID,Yariyan Peyman³,Rezaie Fatemeh¹⁴^ORCID,Kim Sung Won⁵,Sharifi Alireza⁶^ORCID,Alesheikh Ali Asghar⁷^ORCID,Lee Jongchun⁸,Lee Jungsub⁸,Kim Seonhong⁸,Yoo Juhee⁸,Lee Saro¹⁴^ORCID

Affiliation:

1. Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon, Republic of Korea

2. Division of Science Education, College of Education, Kangwon National University, Gangwon-do, Republic of Korea

3. Department of Surveying Engineering, Saghez Branch, Islamic Azad University, Saghez, Iran;

4. Korea University of Science and Technology, Daejeon, Republic of Korea

5. Geology Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon, Republic of Korea

6. Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

7. Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran

8. Indoor Environment and Noise Research Division, Environmental Infrastructure Research Department, National Institute of Environmental Research, Incheon, Republic of Korea

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2021.2022011

Reference106 articles.

1. A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment

2. Spatial modelling of shallow landslide susceptibility: a study from the southern Western Ghats region of Kerala, India.

3. Radon soil–gas as a geological mapping tool: case study from basement complex of Nigeria

4. Akerblom G, Mjones L, Annanmaki M, Magnusson S, Strand T, Ulbak K. 2000. Naturally occurring radioactivity in the Nordic countries-recommendations. The radiation protection authorities in Denmark, Finland, Iceland, Norway and Sweden. https://www.gr.is/wp-content/media/2013/07/NaturallyOccurringRadioactivity.pdf.

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