Skip to main content
eScholarship
Open Access Publications from the University of California

A sequential Bayesian approach for hydrologic model selection and prediction

  • Author(s): Hsu, KL
  • Moradkhani, H
  • Sorooshian, S
  • et al.
Abstract

When a single model is used for hydrologic prediction, it must be capable of estimating system behavior accurately at all times. Multiple-model approaches integrate several model behaviors and, when effective, they can provide better estimates than that of any single model alone. This paper discusses a sequential model fusion strategy that uses the Bayes rule. This approach calculates each model's transient posterior distribution at each time when a new observation is available and merges all model estimates on the basis of each model's posterior probability. This paper demonstrates the feasibility of this approach through case studies that fuse three hydrologic models, auto regressive with exogenous inputs, Sacramento soil moisture accounting, and artificial neural network models, to predict daily watershed streamflow. Copyright 2009 by the American Geophysical Union.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
Current View