Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping.
- DOI
- 10.1007/s11356-024-34691-y
- Published
- 2024 Aug
- Container
- Environmental science and pollution research international
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s11356-024-34691-y,
title = {Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping.},
author = {Nasiri Khiavi A and Vafakhah M},
year = {2024},
journal = {Environmental science and pollution research international},
doi = {10.1007/s11356-024-34691-y},
url = {https://doi.org/10.1007/s11356-024-34691-y}
}RIS
TY - JOUR TI - Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping. AU - Nasiri Khiavi A AU - Vafakhah M PY - 2024 JO - Environmental science and pollution research international DO - 10.1007/s11356-024-34691-y UR - https://doi.org/10.1007/s11356-024-34691-y ER -
APA
A, N. K., & M, V. (2024). Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping.. Environmental science and pollution research international. https://doi.org/10.1007/s11356-024-34691-y
Source records
- pubmed · retrieved 2026-09-25T10:42:24.984Z