TY - JOUR
T1 - A non-linear forecasting system for the Ebro River at Zaragoza, Spain
AU - Pedregal, D. J.
AU - Rivas, R.
AU - Feliu, V.
AU - Sánchez, L.
AU - Linares, A.
PY - 2009/4
Y1 - 2009/4
N2 - This paper addresses the problem of modelling and forecasting river flows and levels based on flood routing type models. Though this is generally considered as a non-linear problem, very often it is treated by linear models. A forecasting system is built for the level and flow measurements registered in the Ebro River at the station of Zaragoza (Spain), with the main purpose of preventing floods in an early stage of development. The model takes advantage of the wealth of data available at the Ebro Hydrographical Confederation and is non-linear in essence. The system is obtained by application of system identification tools, starting from a linear specification and relating the parameters of the model estimated to some transformation of the input in the system. Such transformation requires the application of a Kalman Filter in a particular set up and the full estimation algorithm involves an iterative procedure. The model is fully developed on a data set and is thoroughly validated on a different span of data.
AB - This paper addresses the problem of modelling and forecasting river flows and levels based on flood routing type models. Though this is generally considered as a non-linear problem, very often it is treated by linear models. A forecasting system is built for the level and flow measurements registered in the Ebro River at the station of Zaragoza (Spain), with the main purpose of preventing floods in an early stage of development. The model takes advantage of the wealth of data available at the Ebro Hydrographical Confederation and is non-linear in essence. The system is obtained by application of system identification tools, starting from a linear specification and relating the parameters of the model estimated to some transformation of the input in the system. Such transformation requires the application of a Kalman Filter in a particular set up and the full estimation algorithm involves an iterative procedure. The model is fully developed on a data set and is thoroughly validated on a different span of data.
KW - Environmental systems
KW - Forecasting
KW - Kalman Filter
KW - Parameter estimation
KW - Systems identification
KW - Water efficient use
UR - http://www.scopus.com/inward/record.url?scp=57649224044&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2008.09.010
DO - 10.1016/j.envsoft.2008.09.010
M3 - Article
AN - SCOPUS:57649224044
SN - 1364-8152
VL - 24
SP - 502
EP - 509
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
IS - 4
ER -