Application of periodic autoregressive process to the modeling of the Garonne river flows
| dc.rights.license | open | en_US |
| dc.contributor.author | URSU, Eugen
IDREF: 228232201 | |
| hal.structure.identifier | Groupe de Recherche en Economie Théorique et Appliquée [GREThA] | |
| dc.contributor.author | PEREAU, Jean-Christophe
IDREF: 086314629 | |
| dc.date.accessioned | 2020-02-19T21:24:30Z | |
| dc.date.available | 2020-02-19T21:24:30Z | |
| dc.date.issued | 2016 | |
| dc.identifier.issn | 14363240 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/3593 | |
| dc.description.abstractEn | Accurate forecasting of river flows is one of the most important applications in hydrology, especially for the management of reservoir systems. To capture the seasonal variations in river flow statistics, this paper develops a robust modeling approach to identify and to estimate periodic autoregressive (PAR) model in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on residual autocovariances. A genetic algorithm with Bayes information criterion is used to identify the optimal PAR model. The method is applied to average monthly and quarter-monthly flow data (1959–2010) for the Garonne river in the southwest of France. Results show that the accuracy of forecasts is improved in the robust model with respect to the unrobust model for the quarter-monthly flows. By reducing the number of parameters to be estimated, the principle of parsimony favors the choice of the robust approach. | |
| dc.language.iso | EN | en_US |
| dc.subject.en | Statistics | |
| dc.subject.en | Genetic Algorithms | |
| dc.subject.en | Least-Squares Estimator | |
| dc.subject.en | France | |
| dc.subject.en | Periodic Time Series | |
| dc.subject.en | Bayes Information Criterion | |
| dc.subject.en | Estimation Method | |
| dc.subject.en | Flow Modeling | |
| dc.subject.en | Flow Of Water | |
| dc.subject.en | Garonne River | |
| dc.subject.en | Genetic Algorithm | |
| dc.subject.en | Periodic Autoregressive Process | |
| dc.subject.en | Periodic Time | |
| dc.subject.en | Reservoir Management | |
| dc.subject.en | Reservoir Systems | |
| dc.subject.en | River Flow | |
| dc.subject.en | River Flows Analysis | |
| dc.subject.en | Rivers | |
| dc.subject.en | Robust Estimation | |
| dc.subject.en | Seasonal Variation | |
| dc.subject.en | Time Series | |
| dc.subject.en | Time Series Analysis | |
| dc.title.en | Application of periodic autoregressive process to the modeling of the Garonne river flows | |
| dc.title.alternative | Stoch. Environ. Res. Risk Assess. | en_US |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1007/s00477-015-1193-3 | en_US |
| dc.subject.hal | Économie et finance quantitative [q-fin] | en_US |
| bordeaux.journal | Stochastic Environmental Research and Risk Assessment | en_US |
| bordeaux.page | 1785-1795 | en_US |
| bordeaux.volume | 30 | en_US |
| bordeaux.hal.laboratories | Groupe de Recherche en Economie Théorique et Appliquée (GREThA) - UMR 5113 | en_US |
| bordeaux.issue | 7 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| hal.identifier | hal-03122627 | |
| hal.version | 1 | |
| hal.date.transferred | 2021-01-27T09:48:33Z | |
| hal.export | true | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Stochastic%20Environmental%20Research%20and%20Risk%20Assessment&rft.date=2016&rft.volume=30&rft.issue=7&rft.spage=1785-1795&rft.epage=1785-1795&rft.eissn=14363240&rft.issn=14363240&rft.au=URSU,%20Eugen&PEREAU,%20Jean-Christophe&rft.genre=article |
Fichier(s) constituant ce document
| Fichiers | Taille | Format | Vue |
|---|---|---|---|
|
Il n'y a pas de fichiers associés à ce document. |
|||