Machine learning prediction of arsenic natural background level heterogeneity in shallow groundwater: Integrating in-situ hydrochemistry and spatial proxies at the meso-scale
- DOI
- 10.1016/j.jconhyd.2026.105154
- Published
- 2027-01
- Container
- Journal of Contaminant Hydrology
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jconhyd.2026.105154,
title = {Machine learning prediction of arsenic natural background level heterogeneity in shallow groundwater: Integrating in-situ hydrochemistry and spatial proxies at the meso-scale},
author = {Laura Landi and Joel Podgorski and Michael Berg and Marco Rotiroti and Chiara Zanotti and Alessandro Amorosi and Enrico Dinelli and Maria Filippini},
year = {2027},
journal = {Journal of Contaminant Hydrology},
doi = {10.1016/j.jconhyd.2026.105154},
url = {https://doi.org/10.1016/j.jconhyd.2026.105154}
}RIS
TY - JOUR TI - Machine learning prediction of arsenic natural background level heterogeneity in shallow groundwater: Integrating in-situ hydrochemistry and spatial proxies at the meso-scale AU - Laura Landi AU - Joel Podgorski AU - Michael Berg AU - Marco Rotiroti AU - Chiara Zanotti AU - Alessandro Amorosi AU - Enrico Dinelli AU - Maria Filippini PY - 2027 JO - Journal of Contaminant Hydrology DO - 10.1016/j.jconhyd.2026.105154 UR - https://doi.org/10.1016/j.jconhyd.2026.105154 ER -
APA
Landi, L., Podgorski, J., Berg, M., Rotiroti, M., Zanotti, C., Amorosi, A., Dinelli, E., & Filippini, M. (2027). Machine learning prediction of arsenic natural background level heterogeneity in shallow groundwater: Integrating in-situ hydrochemistry and spatial proxies at the meso-scale. Journal of Contaminant Hydrology. https://doi.org/10.1016/j.jconhyd.2026.105154
Source records
- crossref · retrieved 2026-11-07T16:21:26.658Z