000084166 001__ 84166
000084166 005__ 20210507081948.0
000084166 0247_ $$2doi$$a10.1007/s11269-018-2114-2
000084166 0248_ $$2sideral$$a108321
000084166 037__ $$aART-2018-108321
000084166 041__ $$aeng
000084166 100__ $$0(orcid)0000-0002-9052-9674$$aCebrián, A.C.$$uUniversidad de Zaragoza
000084166 245__ $$aDynamic regression model for hourly river level forecasting under risk situations: An application to the Ebro River
000084166 260__ $$c2018
000084166 5060_ $$aAccess copy available to the general public$$fUnrestricted
000084166 5203_ $$aThis work proposes a new statistical modelling approach to forecast the hourly river level at a gauging station, under potential flood risk situations and over a medium-term prediction horizon (around three days). For that aim we introduce a new model, the switching regression model with ARMA errors, which takes into account the serial correlation structure of the hourly level series, and the changing time delay between them. A whole modelling approach is developed, including a two-step estimation, which improves the medium-term prediction performance of the model, and uncertainty measures of the predictions. The proposed model not only provides predictions for longer periods than other statistical models, but also helps to understand the physics of the river, by characterizing the relationship between the river level in a gauging station and its influential factors. This approach is applied to forecast the Ebro River level at Zaragoza (Spain), using as input the series at Tudela. The approach has shown to be useful and the resulting model provides satisfactory hourly predictions, which can be fast and easily updated, together with their confidence intervals. The fitted model outperforms the predictions from other statistical and numerical models, specially in long prediction horizons.
000084166 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E22$$9info:eu-repo/grantAgreement/ES/MICINN/MTM2017-83812-P
000084166 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000084166 590__ $$a2.987$$b2018
000084166 591__ $$aWATER RESOURCES$$b18 / 91 = 0.198$$c2018$$dQ1$$eT1
000084166 591__ $$aENGINEERING, CIVIL$$b24 / 132 = 0.182$$c2018$$dQ1$$eT1
000084166 592__ $$a1.097$$b2018
000084166 593__ $$aWater Science and Technology$$c2018$$dQ1
000084166 593__ $$aCivil and Structural Engineering$$c2018$$dQ1
000084166 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000084166 700__ $$0(orcid)0000-0002-7974-7435$$aAbaurrea, J.$$uUniversidad de Zaragoza
000084166 700__ $$0(orcid)0000-0002-0174-789X$$aAsín, J.$$uUniversidad de Zaragoza
000084166 700__ $$aSegarra, E.
000084166 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000084166 773__ $$g33, 2 (2018), 523–537$$pWater resour. manag.$$tWATER RESOURCES MANAGEMENT$$x0920-4741
000084166 8564_ $$s2084558$$uhttps://zaguan.unizar.es/record/84166/files/texto_completo.pdf$$yPostprint
000084166 8564_ $$s54301$$uhttps://zaguan.unizar.es/record/84166/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000084166 909CO $$ooai:zaguan.unizar.es:84166$$particulos$$pdriver
000084166 951__ $$a2021-05-07-08:11:15
000084166 980__ $$aARTICLE