A Bayesian approach to stochastic capture zone delineation incorporating tracer arrival times, conductivity measurements, and hydraulic head observations

By Luc Feyen, J. Jaime Gómez-Hernández, P. J. Ribeiro, Keith Beven, F. D. Smedt. 2003

54 citations recorded by the source corpus.

Subfields: Environmental Engineering, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/41c68c560c4567549e217e42e383af9a0608116e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. John Doherty
2. Catherine R. Moore
3. Philipp C. Leube
4. Andreas Geiges
5. Wolfgang Nowak
6. M. Tonkin
7. Sergey Oladyshkin
8. Jasper A. Vrugt

In Mechanical Engineering

1. John Doherty
2. Catherine R. Moore
3. Wolfgang Nowak

In Ocean Engineering

1. Wolfgang Nowak
2. John Doherty
3. Catherine R. Moore

Citations in the corpus

• Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design (2012, methodology): “Several recent studies, including Feyen et al. [2003], Nowak et al. [2010], and Murakami et al.”
• Role of the calibration process in reducing model predictive error (2005, background): “Thus, for example, if a model is built to make predictions of contaminant transport, then its performance in this regard will be better served if it is calibrated against historical contaminant concentration data than if it is calibrated solely against groundwater heads [see, e.g., Harvey and Goreli”