Predicting and assessing the distribution of high-fluoride groundwater in China using multiple machine learning models: A big data-driven geospatial analysis
- DOI
- 10.1016/j.ecoenv.2026.120627
- Published
- 2026-09
- Container
- Ecotoxicology and Environmental Safety
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.ecoenv.2026.120627,
title = {Predicting and assessing the distribution of high-fluoride groundwater in China using multiple machine learning models: A big data-driven geospatial analysis},
author = {Mengyao Su and Qingbo Wang and Yunzhu Liu and Xin Wang and Chao Zhang and Xin Zhang and Chang Liu and Yanmei Yang and Peng Luo and Yue Gao and Yanhui Gao},
year = {2026},
journal = {Ecotoxicology and Environmental Safety},
doi = {10.1016/j.ecoenv.2026.120627},
url = {https://doi.org/10.1016/j.ecoenv.2026.120627}
}RIS
TY - JOUR TI - Predicting and assessing the distribution of high-fluoride groundwater in China using multiple machine learning models: A big data-driven geospatial analysis AU - Mengyao Su AU - Qingbo Wang AU - Yunzhu Liu AU - Xin Wang AU - Chao Zhang AU - Xin Zhang AU - Chang Liu AU - Yanmei Yang AU - Peng Luo AU - Yue Gao AU - Yanhui Gao PY - 2026 JO - Ecotoxicology and Environmental Safety DO - 10.1016/j.ecoenv.2026.120627 UR - https://doi.org/10.1016/j.ecoenv.2026.120627 ER -
APA
Su, M., Wang, Q., Liu, Y., Wang, X., Zhang, C., Zhang, X., Liu, C., Yang, Y., Luo, P., Gao, Y., & Gao, Y. (2026). Predicting and assessing the distribution of high-fluoride groundwater in China using multiple machine learning models: A big data-driven geospatial analysis. Ecotoxicology and Environmental Safety. https://doi.org/10.1016/j.ecoenv.2026.120627
Source records
- crossref · retrieved 2026-09-25T12:30:01.976Z