Toward Improved Hydrologic Prediction with Reduced Uncertainty Using Sequential Multi-Model Combination
Publication: World Environmental and Water Resources Congress 2008: Ahupua'A
Abstract
The contemporary usage of hydrologic models has been to rely on a single model to perform the simulation and predictions. Despite the tremendous progress, efforts and investment put into developing more hydrologic models, there is no convincing claim that any particular model in existence is superior to other models for various applications and under all circumstances. This results to reducing the size of the plausible model space and often leads to predictions that may well-represent some phenomena or events at the expenses of others. Assessment of predictive uncertainty based on a single model is subject to statistical bias and most likely underestimation of uncertainty. This endorses the implementation of multi-model methods for more accurate estimation of uncertainty in hydrologic prediction. In this study, we present two methods for the combination of multiple model predictors using Bayesian Model Averaging (BMA) and Sequential Bayesian Model Combination (SBMC). Both methods are statistical schemes to infer a combined probabilistic prediction that possess more reliability and skill than the original model members produced by several competing models. This paper discusses the features of both methods and explains how the limitation of BMA can be overcome by SBMC. Three hydrologic models are considered and it is shown that multi-model combination can result in higher prediction accuracy than individual models.
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Copyright
© 2008 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Business management
- Continuum mechanics
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Financial management
- Hydraulic models
- Hydrologic models
- Investments
- Mathematics
- Model accuracy
- Models (by type)
- Motion (dynamics)
- Practice and Profession
- Simulation models
- Solid mechanics
- Statistical analysis (by type)
- Statistics
- Uncertainty principles
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