Joshi, Deepti; St-Hilaire, André; Daigle, Anik et Ouarda, Taha B. M. J.
(2013).
*Databased comparison of Sparse Bayesian Learning and Multiple Linear Regression for statistical downscaling of low flow indices.*
*Journal of Hydrology*
, vol. 488
.
pp. 136-149.
DOI: 10.1016/j.jhydrol.2013.02.040.

## Résumé

This study attempts to compare the performance of two statistical downscaling frameworks in downscaling hydrological indices (descriptive statistics) characterizing the low flow regimes of three rivers in Eastern Canada - Moisie, Romaine and Ouelle. The statistical models selected are Relevance Vector Machine (RVM), an implementation of Sparse Bayesian Learning, and the Automated Statistical Downscaling tool (ASD), an implementation of Multiple Linear Regression. Inputs to both frameworks involve climate variables significantly (alpha = 0.05) correlated with the indices. These variables were processed using Canonical Correlation Analysis and the resulting canonical variates scores were used as input to RVM to estimate the selected low flow indices. In ASD, the significantly correlated climate variables were subjected to backward stepwise predictor selection and the selected predictors were subsequently used to estimate the selected low flow indices using Multiple Linear Regression. With respect to the correlation between climate variables and the selected low flow indices, it was observed that all indices are influenced, primarily, by wind components (Vertical, Zonal and Meridonal) and humidity variables (Specific and Relative Humidity). The downscaling performance of the framework involving RVM was found to be better than ASD in terms of Relative Root Mean Square Error, Relative Mean Absolute Bias and Coefficient of Determination. In all cases, the former resulted in less variability of the performance indices between calibration and validation sets, implying better generalization ability than for the latter.

Type de document: | Article |
---|---|

Mots-clés libres: | downscaling; Low flows; canonical correlation analysis; Sparse Bayesian Learning; Multiple Linear Regression |

Centre: | Centre Eau Terre Environnement |

Date de dépôt: | 05 déc. 2016 21:00 |

Dernière modification: | 05 déc. 2016 21:00 |

URI: | https://espace.inrs.ca/id/eprint/3453 |

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