Uncertainty Analysis of a Temperature-Index Snowmelt Model Using Bayesian Networks

11 pages•Published: September 20, 2018

Brahim Boutkhamouine, Hélène Roux, François Pérès and Willem Vervoort

Abstract

Uncertainty analysis of hydrological models often requires a large number of model runs, which can be time consuming and computationally intensive. In order to reduce the number of runs required for uncertainty prediction, we explore in this study the potential of Bayesian Networks (BNs). A BN is created using a simple version of Temperature-Index Snowmelt Model. Next, uncertainty analysis is performed using both the BN method and Monte-Carlo (MC) simulations. The results show that BN method gives similar results to the MC method and can be used for real-time applications.

Keyphrases: bayesian networks (bns), deterministic models (dm), uncertainty analysis

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 310-320.

Links:	https://easychair.org/publications/paper/GxXd
	https://doi.org/10.29007/997w

BibTeX entry

@inproceedings{HIC2018:Uncertainty_Analysis_Temperature_Index,
  author    = {Brahim Boutkhamouine and Hélène Roux and François Pérès and Willem Vervoort},
  title     = {Uncertainty Analysis of a Temperature-Index Snowmelt Model Using Bayesian Networks},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/GxXd},
  doi       = {10.29007/997w},
  pages     = {310-320},
  year      = {2018}}

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