A hybrid data-driven framework for diagnosing contributing factors for soil heavy metal contaminations using machine learning and spatial clustering analysis.
- DOI
- 10.1016/j.jhazmat.2022.129324
- Published
- 2022 Sep 5
- 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.2022.129324,
title = {A hybrid data-driven framework for diagnosing contributing factors for soil heavy metal contaminations using machine learning and spatial clustering analysis.},
author = {Huang G and Wang X and Chen D and Wang Y and Zhu S and Zhang T and Liao L and Tian Z and Wei N},
year = {2022},
journal = {Journal of hazardous materials},
doi = {10.1016/j.jhazmat.2022.129324},
url = {https://doi.org/10.1016/j.jhazmat.2022.129324}
}RIS
TY - JOUR TI - A hybrid data-driven framework for diagnosing contributing factors for soil heavy metal contaminations using machine learning and spatial clustering analysis. AU - Huang G AU - Wang X AU - Chen D AU - Wang Y AU - Zhu S AU - Zhang T AU - Liao L AU - Tian Z AU - Wei N PY - 2022 JO - Journal of hazardous materials DO - 10.1016/j.jhazmat.2022.129324 UR - https://doi.org/10.1016/j.jhazmat.2022.129324 ER -
APA
G, H., X, W., D, C., Y, W., S, Z., T, Z., L, L., Z, T., & N, W. (2022). A hybrid data-driven framework for diagnosing contributing factors for soil heavy metal contaminations using machine learning and spatial clustering analysis.. Journal of hazardous materials. https://doi.org/10.1016/j.jhazmat.2022.129324
Source records
- pubmed · retrieved 2026-09-26T21:24:15.746Z