Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction.
- DOI
- 10.1007/s11356-024-35481-2
- Published
- 2024 Dec
- 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/s11356-024-35481-2,
title = {Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction.},
author = {Bhattacharjee R and Gaur S and Chander S and Ohri A and Srivastava PK and Mishra A},
year = {2024},
journal = {Environmental science and pollution research international},
doi = {10.1007/s11356-024-35481-2},
url = {https://doi.org/10.1007/s11356-024-35481-2}
}RIS
TY - JOUR TI - Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction. AU - Bhattacharjee R AU - Gaur S AU - Chander S AU - Ohri A AU - Srivastava PK AU - Mishra A PY - 2024 JO - Environmental science and pollution research international DO - 10.1007/s11356-024-35481-2 UR - https://doi.org/10.1007/s11356-024-35481-2 ER -
APA
R, B., S, G., S, C., A, O., PK, S., & A, M. (2024). Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction.. Environmental science and pollution research international. https://doi.org/10.1007/s11356-024-35481-2
Source records
- pubmed · retrieved 2026-11-10T12:22:42.957Z