A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate.
- DOI
- 10.1016/j.scitotenv.2021.151752
- Published
- 2022 Apr 20
- 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.2021.151752,
title = {A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate.},
author = {Qiu C and Su L and Zou Q and Geng X},
year = {2022},
journal = {The Science of the total environment},
doi = {10.1016/j.scitotenv.2021.151752},
url = {https://doi.org/10.1016/j.scitotenv.2021.151752}
}RIS
TY - JOUR TI - A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate. AU - Qiu C AU - Su L AU - Zou Q AU - Geng X PY - 2022 JO - The Science of the total environment DO - 10.1016/j.scitotenv.2021.151752 UR - https://doi.org/10.1016/j.scitotenv.2021.151752 ER -
APA
C, Q., L, S., Q, Z., & X, G. (2022). A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate.. The Science of the total environment. https://doi.org/10.1016/j.scitotenv.2021.151752
Source records
- pubmed · retrieved 2026-09-26T05:45:28.848Z