BAYESIAN DISCRIMINATION OF HYDROLOGIC FORECASTING MODELS BASED ON THE KALMAN FILTER.

Juan B. Valdes, Jesus M. Velasquez, Ignacio Rodriguez-Iturbe

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

The Kalman filter algorithm very well suits real time prediction of streamflow. The state of the system is assumed to be either the ordinates of the response function of the system or streamflows themselves. In the first case assumptions have to be made about the initial state of the system, the lag structure of the model and the covariance matrix of the measurement noise. In this paper the use of Bayesian theory is proposed to discriminate alternative assumptions on the values of these variables. Controlled and real world experiments were carried out to examine the performance of these discrimination criteria and the results were quite satisfactory.

Original languageEnglish (US)
Pages369-384
Number of pages16
StatePublished - Jan 1 1978
EventAppl of Kalman Filter to Hydrol, Hydraul, and Water Resour, Proc of AGU (Am Geophys Union) Chapman Conf - Pittsburgh, PA, USA
Duration: May 22 1978May 24 1978

Other

OtherAppl of Kalman Filter to Hydrol, Hydraul, and Water Resour, Proc of AGU (Am Geophys Union) Chapman Conf
CityPittsburgh, PA, USA
Period5/22/785/24/78

ASJC Scopus subject areas

  • Engineering(all)

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    Valdes, J. B., Velasquez, J. M., & Rodriguez-Iturbe, I. (1978). BAYESIAN DISCRIMINATION OF HYDROLOGIC FORECASTING MODELS BASED ON THE KALMAN FILTER.. 369-384. Paper presented at Appl of Kalman Filter to Hydrol, Hydraul, and Water Resour, Proc of AGU (Am Geophys Union) Chapman Conf, Pittsburgh, PA, USA, .