Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach

By Wei Gong, Hoshin V. Gupta, Dawen Yang, Kumar Sricharan, A. Hero. 2013

122 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/251b779800c86d0b23ace785ee44cebef0d840de

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. M. Clark
2. Chaopeng Shen
3. John M. Quilty
4. Wei Gong
5. Dawen Yang
6. Jan Adamowski
7. Grey Nearing
8. Hoshin V. Gupta

In Global and Planetary Change

1. Chaopeng Shen
2. M. Clark
3. Wei Gong
4. Dawen Yang
5. John M. Quilty
6. Hoshin V. Gupta
7. Grey Nearing
8. Jan Adamowski

In Water Science and Technology

1. M. Clark
2. Chaopeng Shen
3. Wei Gong
4. Dawen Yang
5. Grey Nearing
6. Hoshin V. Gupta
7. Steven V. Weijs
8. John M. Quilty

Citations in the corpus

• Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling (2016, background): “…et al., 2015], psychology [Harel et al., 2007], image and video analysis [Liu and Karam, 2005; Korshunov and Ooi, 2011], geophysics [Chen et al., 2008], agriculture [Mishra et al., 2013], and water resources [Sharma, 2000a; Maier et al., 2006; Gong et al., 2013] (among many fields of application).”
• A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “In a unique series of studies, Gong et al. (2013) proposed a quantitative method, grounded in information theory, to separate out uncertainties due to inputs andmodel structural error.”
• A philosophical basis for hydrological uncertainty (2016, background): “…models (which, as argued above, should never be the objective of any applied science), we might be tempted to think of aleatory uncertainty as referring to uncertainty due to observations and epistemic uncertainty as referring to uncertainty about phenomenological laws (e.g. Gong et al. 2013).”
• Estimating information entropy for hydrological data: One‐dimensional case (2014, methodology): “…the entropy estimation methods to a widely used 40 year (October 1948 to September 1988) daily time step data set from the 1944 km2 humid Leaf River basin located north of Collins, Mississippi, USA [see, e.g., Sorooshian et al., 1993; Thiemann et al., 2001; Vrugt et al., 2003; Gong et al., 2013].”