Predicting radon flux density from soil surface using machine learning and GIS data.

Gavriliev S, Petrova T, Miklyaev P, Karfidova E

DOI: 10.1016/j.scitotenv.2023.166348
Published: 2023 Dec 10
Container: The Science of the total environment
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/j.scitotenv.2023.166348,
  title = {Predicting radon flux density from soil surface using machine learning and GIS data.},
  author = {Gavriliev S and Petrova T and Miklyaev P and Karfidova E},
  year = {2023},
  journal = {The Science of the total environment},
  doi = {10.1016/j.scitotenv.2023.166348},
  url = {https://doi.org/10.1016/j.scitotenv.2023.166348}
}

RIS

TY  - JOUR
TI  - Predicting radon flux density from soil surface using machine learning and GIS data.
AU  - Gavriliev S
AU  - Petrova T
AU  - Miklyaev P
AU  - Karfidova E
PY  - 2023
JO  - The Science of the total environment
DO  - 10.1016/j.scitotenv.2023.166348
UR  - https://doi.org/10.1016/j.scitotenv.2023.166348
ER  -

APA

S, G., T, P., P, M., & E, K. (2023). Predicting radon flux density from soil surface using machine learning and GIS data.. The Science of the total environment. https://doi.org/10.1016/j.scitotenv.2023.166348

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pubmed · retrieved 2026-09-25T14:57:56.411Z