Peer-reviewedCC BY-NC-ND 4.0Scientific Reports
Mapping soil salinity and prediction uncertainty across different depths using quantile regression forests
Related
- EU carbon pricing and clean energy markets: evidence from cross-quantilogram and multivariate time-varying quantile regression
- An intelligent weighted adaptive regression process for reliable and uncertainty aware drought forecasting
- Input-data perturbation in groundwater level prediction: a controlled experiment on accuracy–reliability trade-offs among data augmentation and Bayesian-type uncertainty estimation methods
- Time Series Fuzzy Bayesian Logistic Regression for Wildfire Event Prediction
- Prediction of fast, slow, and average shear-wave velocities from well logs using artificial neural networks and statistical regression
BibTeX
@article{zhang2026mapping,
title = {{Mapping soil salinity and prediction uncertainty across different depths using quantile regression forests}},
author = {Huarong Zhang and Dashan Han and Xiao Wang},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-77408-6},
url = {https://www.nature.com/articles/s41598-026-77408-6},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Mapping soil salinity and prediction uncertainty across different depths using quantile regression forests
AU - Huarong Zhang
AU - Dashan Han
AU - Xiao Wang
PY - 2026
DA - 2026/10/26
JO - Scientific Reports
DO - 10.1038/s41598-026-77408-6
UR - https://www.nature.com/articles/s41598-026-77408-6
SN - 2045-2322
ER -