Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty
- DOI
- 10.1007/s10661-026-15837-6
- Published
- 2026-08-29
- Container
- Environmental Monitoring and Assessment
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s10661-026-15837-6,
title = {Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty},
author = {Karzan A. Mohammed Hawrami},
year = {2026},
journal = {Environmental Monitoring and Assessment},
doi = {10.1007/s10661-026-15837-6},
url = {https://doi.org/10.1007/s10661-026-15837-6}
}RIS
TY - JOUR TI - Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty AU - Karzan A. Mohammed Hawrami PY - 2026 JO - Environmental Monitoring and Assessment DO - 10.1007/s10661-026-15837-6 UR - https://doi.org/10.1007/s10661-026-15837-6 ER -
APA
Hawrami, K. A. M. (2026). Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-026-15837-6
Source records
- crossref · retrieved 2026-11-10T13:05:16.571Z