Ternynck, Camille; Ben Alaya, Mohamed Ali; Chebana, Fateh 
ORCID: https://orcid.org/0000-0002-3329-8179; Dabo-Niang, Sophie et Ouarda, Taha B. M. J. ORCID: https://orcid.org/0000-0002-0969-063X
(2016).
Streamflow hydrograph classification using functional data analysis.
Journal of Hydrometeorology
, vol. 17
, nº 1.
pp. 327-344.
DOI: 10.1175/JHM-D-14-0200.1.
Résumé
Classification of streamflow hydrographs plays an important role in a large number of hydrological and hydraulic studies. For instance, it allows to make decisions regarding the implementation of hydraulic structures and to characterize different flood types leading to a better understanding of extreme flow behavior. The employed hydrograph classification methods are generally based on a finite number of hydrograph characteristics, and do not include all the available information contained in a discharge time series. In this paper, we adapt and apply two statistical techniques from the theory of functional data classification for the analysis of flood hydrographs. Functional classification directly employs all data of a discharge time series and thus contains all available information on shape, peak and timing. This potentially allows a better understanding and treatment of floods as well as other hydrological phenomena. The considered functional methodology is applied to streamflow datasets from the province of Quebec, Canada. We show that classes obtained using functional approaches have merit and can lead to better representation than those obtained using a multidimensional hierarchical classification method. The considered methodology has the advantage of using the entire information contained in the hydrograph, reducing hence the subjectivity that is inherent in multidimensional analysis on the type and number of characteristics to be used, and diminishing consequently the associated uncertainty.
| Type de document: | Article |
|---|---|
| Mots-clés libres: | xxx |
| Centre: | Centre Eau Terre Environnement |
| Date de dépôt: | 20 déc. 2016 15:57 |
| Dernière modification: | 21 févr. 2022 17:27 |
| URI: | https://espace.inrs.ca/id/eprint/3942 |
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