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Structured Expert Elicitation of Dependence Between River Tributaries Using Nonparametric Bayesian Networks.

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Structured expert judgment effectively quantifies uncertain river discharge correlations. Experts, aided by software, built accurate Bayesian networks, showing this method

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Area of Science:

  • Hydrology
  • Statistics
  • Decision Science

Background:

  • Structured expert judgment is vital for estimating uncertain quantities when data is scarce.
  • Eliciting dependencies between variables using expert judgment is an under-researched area.

Purpose of the Study:

  • To evaluate expert performance in constructing and quantifying nonparametric Bayesian networks for river tributary discharge correlations.
  • To assess the effectiveness of specialized software in aiding experts.

Main Methods:

  • Experts utilized specialized graphical software to build and quantify Bayesian networks.
  • Expert performance was measured using the dependence calibration score and joint distribution likelihood.
  • Individual expert judgments were aggregated into a group opinion (decision maker) based on performance.

Main Results:

  • All experts successfully created and quantified correlation matrices that accurately reflected observed river discharge patterns.
  • Group decision makers performed comparably to the best individual experts.
  • Performance-based weighting became more impactful when incorporating poorly performing experts, highlighting the need for robust scoring rules.

Conclusions:

  • Structured expert judgment, supported by specialized software, is a promising method for quantifying complex dependence structures.
  • Experts can efficiently construct and quantify nonparametric Bayesian networks for hydrological data.
  • Further development of scoring rules is important for optimizing performance-based weighting in expert judgment aggregation.