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Applications of hybrid dynamic Bayesian networks to water reservoir management

Author

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  • Rosa F. Ropero
  • M. Julia Flores
  • Rafael Rumí
  • Pedro A. Aguilera

Abstract

Bayesian networks (BNs) have been widely applied in environmental modelling to predict the behavior of an ecosystem under conditions of change. However, this approximation doesn't take time into consideration. To solve this issue, an extension of BNs, the dynamic Bayesian networks (DBNs), has been developed in mathematics and computer science areas but has scarcely been applied in environmental modelling. This paper presents the application of DBN to water reservoir systems in Andalusia, Spain. The aim is to predict changes in the percent fullness of the reservoirs under the irregular rainfall patterns of Mediterranean watersheds. In comparison to static BNs, DBNs provide results that can be extrapolated to a particular time so that a climate change scenario can be studied in detail over time. Because results are expressed by density functions rather than unique values, several metrics are obtained from the results, including the probability of certain values. This allows the probability that water level in a reservoir reaches a certain level to be directly computed.

Suggested Citation

  • Rosa F. Ropero & M. Julia Flores & Rafael Rumí & Pedro A. Aguilera, 2017. "Applications of hybrid dynamic Bayesian networks to water reservoir management," Environmetrics, John Wiley & Sons, Ltd., vol. 28(1), February.
    • Handle: RePEc:wly:envmet:v:28:y:2017:i:1:n:e2432
    DOI: 10.1002/env.2432
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    Cited by:

    1. Parisa Noorbeh & Abbas Roozbahani & Hamid Kardan Moghaddam, 2020. "Annual and Monthly Dam Inflow Prediction Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2933-2951, July.
    2. Marco Scutari, 2020. "Bayesian network models for incomplete and dynamic data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 397-419, August.

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