Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty
Abstract
In most water resources applications, a single model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integrate expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses are used to assess the presence of multiple dominant processes and the adequacy of a single model, as well as to identify the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. Furthermore, the results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processesmore »
- Authors:
-
- Washington State Univ., Richland, WA (United States)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); State of Washington Water Research Center
- OSTI Identifier:
- 1327118
- Alternate Identifier(s):
- OSTI ID: 1402274
- Report Number(s):
- PNNL-SA-113964
Journal ID: ISSN 0043-1397; KP1703030
- Grant/Contract Number:
- AC05-76RL01830; G11AP20113
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Water Resources Research
- Additional Journal Information:
- Journal Volume: 52; Journal Issue: 4; Journal ID: ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; diagnostic modeling; structural uncertainty; Hierarchical Mixture of Experts; model averaging; model adequacy
Citation Formats
Moges, Edom, Demissie, Yonas, and Li, Hong-Yi. Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty. United States: N. p., 2016.
Web. doi:10.1002/2015WR018266.
Moges, Edom, Demissie, Yonas, & Li, Hong-Yi. Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty. United States. https://doi.org/10.1002/2015WR018266
Moges, Edom, Demissie, Yonas, and Li, Hong-Yi. Fri .
"Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty". United States. https://doi.org/10.1002/2015WR018266. https://www.osti.gov/servlets/purl/1327118.
@article{osti_1327118,
title = {Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty},
author = {Moges, Edom and Demissie, Yonas and Li, Hong-Yi},
abstractNote = {In most water resources applications, a single model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integrate expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses are used to assess the presence of multiple dominant processes and the adequacy of a single model, as well as to identify the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. Furthermore, the results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processes are witnessed through diagnostic measures. Whereas, the diagnostics and aggregated performance measures prove that French Broad has a homogeneous catchment response, making the single model adequate to capture the response.},
doi = {10.1002/2015WR018266},
journal = {Water Resources Research},
number = 4,
volume = 52,
place = {United States},
year = {Fri Mar 04 00:00:00 EST 2016},
month = {Fri Mar 04 00:00:00 EST 2016}
}
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