Title :

Streamflow forecasting using functional regression

Author(s) :

Masselot, Pierre [Auteur]
Institut National de la Recherche Scientifique [Québec] [INRS]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Lille économie management - UMR 9221 [LEM]
Chebana, Fateh [Auteur]
Institut National de la Recherche Scientifique [Québec] [INRS]
Ouarda, Taha [Auteur]
Masdar Institute of Science and Technology [Abu Dhabi]

Journal title :

Journal of Hydrology

Pages :

754–766

Publisher :

Elsevier

Publication date :

2016-04

ISSN :

0022-1694

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]
Sciences de l'environnement/Milieux et Changements globaux

English abstract : [en]

Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points ...
Show more >Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL