Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors
By David McInerney, Mark A. Thyer, Dmitri Kavetski, Julien Lerat, George A. Kuczera. 2017
142 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/ebae376a8cad496a95c8d935ebc560e5470ef4a9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
- C. Prieto
- Raúl Medina
- E. Garcia
- Nataliya Le Vine
- Dmitri Kavetski
- Jasper A. Vrugt
- M. Clark
- Keith Beven
In Water Science and Technology
- C. Prieto
- Raúl Medina
- E. Garcia
- Nataliya Le Vine
- Dmitri Kavetski
- Jasper A. Vrugt
- M. Clark
- George A. Kuczera
Citations in the corpus
- Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests (2019, background): “…respect, the adequacy metrics contribute toward better probabilistic prediction assessment, which currently includes the probabilistic NSE (Bulygina et al., 2009), the continuous rank probability score (Hersbach, 2000), reliability and precision metrics (McInerney et al., 2017), and other metrics.”