Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria
<p>Accurate predictions of seasonal low flows are critical for a number of water management tasks that require inferences about water quality and the ecological status of water bodies. This paper proposes an extreme gradient tree boosting model (XGBoost) for predicting monthly low flow in unga...
Main Authors: | J. Laimighofer, M. Melcher, G. Laaha |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2022-09-01
|
Series: | Hydrology and Earth System Sciences |
Online Access: | https://hess.copernicus.org/articles/26/4553/2022/hess-26-4553-2022.pdf |
Similar Items
-
Parsimonious statistical learning models for low-flow estimation
by: J. Laimighofer, et al.
Published: (2022-01-01) -
Uncertainty contributions to low-flow projections in Austria
by: J. Parajka, et al.
Published: (2016-05-01) -
Bidual Representation of Expectiles
by: Alejandro Balbás, et al.
Published: (2023-12-01) -
Expectile-Based Capital Allocation
by: Khalil Said
Published: (2023-07-01) -
Expectile Regression With Errors-in-Variables
by: Xiaoxia He, et al.
Published: (2023-01-01)