Dépôt numérique
RECHERCHER

Multivariate missing data in hydrology – Review and applications.

Téléchargements

Téléchargements par mois depuis la dernière année

Plus de statistiques...

Ben Aissia, Mohamed Aymen; Chebana, Fateh ORCID logoORCID: https://orcid.org/0000-0002-3329-8179 et Ouarda, Taha B. M. J. ORCID logoORCID: https://orcid.org/0000-0002-0969-063X (2017). Multivariate missing data in hydrology – Review and applications. Advances in Water Resources , vol. 110 . pp. 299-309. DOI: 10.1016/j.advwatres.2017.10.002.

[thumbnail of P003228.pdf]
Prévisualisation
PDF
Disponible sous licence Creative Commons Attribution Non-commercial No Derivatives.

Télécharger (679kB) | Prévisualisation

Résumé

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Type de document: Article
Mots-clés libres: distribution functions; flood control; hydrology; information management; water resources; hydrological frequency analysis; multivariant analysis
Centre: Centre Eau Terre Environnement
Date de dépôt: 28 nov. 2017 14:45
Dernière modification: 21 févr. 2022 17:35
URI: https://espace.inrs.ca/id/eprint/6376

Gestion Actions (Identification requise)

Modifier la notice Modifier la notice