Learning Bayesian Networks from Deterministic Rainfall–Runoff Models and Monte Carlo Simulation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008

AUTHORS

L. Garrote , M. Molina , L. Mediero

ABSTRACT

A mixed approach based on the combination of deterministic physically based models and probabilistic data-driven models for flood forecasting is presented. The approach uses a Bayesian network built upon the results of a deterministic rainfall–runoff model for real-time decision support. The data set for the calibration and validation of the Bayesian model is obtained through a Monte Carlo simulation technique, combining a stochastic rainfall generator and a deterministic rainfall–runoff model. The methodology allows making probabilistic discharge forecasts in real time using an uncertain quantitative precipitation forecast. The validation experiments made show that the data-driven model can approximate the probability distribution of future discharge that would be obtained with the physically based model applying ensemble prediction techniques, but in a much shorter time. More... »

PAGES

375-388

References to SciGraph publications

  • 1999. A Multiagent System for Emergency Management in Floods in MULTIPLE APPROACHES TO INTELLIGENT SYSTEMS
  • 2005. Hydrologic Models for Emergency Decision Support Using Bayesian Networks in SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY
  • Book

    TITLE

    Practical Hydroinformatics

    ISBN

    978-3-540-79880-4
    978-3-540-79881-1

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-79881-1_27

    DOI

    http://dx.doi.org/10.1007/978-3-540-79881-1_27

    DIMENSIONS

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