Probabilistic Forecasts Using Bayesian Networks Calibrated With Deterministic Rainfall-Runoff Models View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2007

AUTHORS

LUIS GARROTE , MARTÍN MOLINA , LUIS MEDIERO

ABSTRACT

- A flood forecasting approach based on the combination of Bayesian networks and physically-based deterministic models is presented.Bayesian networks are data-driven models where the joint probability distribution of a set of related variables is inferred from observations. Their application to flood forecasting is limited because basins with long data sets for calibration or validation of this type of models are relatively scarce. To solve this problem, the data set for the calibration and validation is obtained through Monte-Carlo simulation, combining a stochastic rainfall generator and a deterministic rainfall-runoff model. The approach has been tested successfully in the Spanish Mediterranean region. More... »

PAGES

173-183

References to SciGraph publications

  • 2005. Hydrologic Models for Emergency Decision Support Using Bayesian Networks in SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY
  • 1998. Learning dynamic Bayesian networks in ADAPTIVE PROCESSING OF SEQUENCES AND DATA STRUCTURES
  • 2003. A Multi-agent System for Emergency Decision Support in INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING
  • Book

    TITLE

    Extreme Hydrological Events: New Concepts for Security

    ISBN

    978-1-4020-5739-7

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-1-4020-5741-0_13

    DOI

    http://dx.doi.org/10.1007/978-1-4020-5741-0_13

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1010915053


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