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

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

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

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

Research output: Contribution to conference › Paper › peer-review

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 language	English (US)
Pages	369-384
Number of pages	16
State	Published - 1978
Externally published	Yes
Event	Appl of Kalman Filter to Hydrol, Hydraul, and Water Resour, Proc of AGU (Am Geophys Union) Chapman Conf - Pittsburgh, PA, USA Duration: May 22 1978 → May 24 1978

Other

Other	Appl of Kalman Filter to Hydrol, Hydraul, and Water Resour, Proc of AGU (Am Geophys Union) Chapman Conf
City	Pittsburgh, PA, USA
Period	5/22/78 → 5/24/78

ASJC Scopus subject areas

General Engineering

Cite this

BAYESIAN DISCRIMINATION OF HYDROLOGIC FORECASTING MODELS BASED ON THE KALMAN FILTER. / Valdes, Juan B.; Velasquez, Jesus M.; Rodriguez-Iturbe, Ignacio.
1978. 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.

Research output: Contribution to conference › Paper › peer-review

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year = "1978",

language = "English (US)",

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AU - Velasquez, Jesus M.

AU - Rodriguez-Iturbe, Ignacio

PY - 1978

Y1 - 1978

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AB - 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.

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Y2 - 22 May 1978 through 24 May 1978

ER -

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

Abstract

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ASJC Scopus subject areas

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