Machine learning and remote sensing for soil heavy metal(loid) risk assessment: A systematic review of multi-source data fusion, benchmarking, and explainable AI.
- DOI
- 10.1016/j.jhazmat.2026.143536
- Published
- 2026 Sep 7
- Container
- Journal of hazardous materials
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jhazmat.2026.143536,
title = {Machine learning and remote sensing for soil heavy metal(loid) risk assessment: A systematic review of multi-source data fusion, benchmarking, and explainable AI.},
author = {Tutul MTE and Datta SD},
year = {2026},
journal = {Journal of hazardous materials},
doi = {10.1016/j.jhazmat.2026.143536},
url = {https://doi.org/10.1016/j.jhazmat.2026.143536}
}RIS
TY - JOUR TI - Machine learning and remote sensing for soil heavy metal(loid) risk assessment: A systematic review of multi-source data fusion, benchmarking, and explainable AI. AU - Tutul MTE AU - Datta SD PY - 2026 JO - Journal of hazardous materials DO - 10.1016/j.jhazmat.2026.143536 UR - https://doi.org/10.1016/j.jhazmat.2026.143536 ER -
APA
MTE, T., & SD, D. (2026). Machine learning and remote sensing for soil heavy metal(loid) risk assessment: A systematic review of multi-source data fusion, benchmarking, and explainable AI.. Journal of hazardous materials. https://doi.org/10.1016/j.jhazmat.2026.143536
Source records
- pubmed · retrieved 2026-09-25T22:53:26.740Z