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

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

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

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)
Title of host publicationUnknown Host Publication Title
PublisherUniv of Pittsburgh, Dep of Civ Eng, Stochastic Hydraul Program
Pages369-384
Number of pages16
StatePublished - 1978
Externally publishedYes
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

Fingerprint

Covariance matrix
Kalman filters
Experiments

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Valdes, J. B., Velasquez, J. M., & Rodriguez-Iturbe, I. (1978). BAYESIAN DISCRIMINATION OF HYDROLOGIC FORECASTING MODELS BASED ON THE KALMAN FILTER. In Unknown Host Publication Title (pp. 369-384). Univ of Pittsburgh, Dep of Civ Eng, Stochastic Hydraul Program.

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

Unknown Host Publication Title. Univ of Pittsburgh, Dep of Civ Eng, Stochastic Hydraul Program, 1978. p. 369-384.

Research output: Chapter in Book/Report/Conference proceedingChapter

Valdes, JB, Velasquez, JM & Rodriguez-Iturbe, I 1978, BAYESIAN DISCRIMINATION OF HYDROLOGIC FORECASTING MODELS BASED ON THE KALMAN FILTER. in Unknown Host Publication Title. Univ of Pittsburgh, Dep of Civ Eng, Stochastic Hydraul Program, pp. 369-384, Appl of Kalman Filter to Hydrol, Hydraul, and Water Resour, Proc of AGU (Am Geophys Union) Chapman Conf, Pittsburgh, PA, USA, 5/22/78.
Valdes JB, Velasquez JM, Rodriguez-Iturbe I. BAYESIAN DISCRIMINATION OF HYDROLOGIC FORECASTING MODELS BASED ON THE KALMAN FILTER. In Unknown Host Publication Title. Univ of Pittsburgh, Dep of Civ Eng, Stochastic Hydraul Program. 1978. p. 369-384
Valdes, Juan B ; Velasquez, Jesus M. ; Rodriguez-Iturbe, Ignacio. / BAYESIAN DISCRIMINATION OF HYDROLOGIC FORECASTING MODELS BASED ON THE KALMAN FILTER. Unknown Host Publication Title. Univ of Pittsburgh, Dep of Civ Eng, Stochastic Hydraul Program, 1978. pp. 369-384
@inbook{6773801448f046d1b18a8b3e5a090bdf,
title = "BAYESIAN DISCRIMINATION OF HYDROLOGIC FORECASTING MODELS BASED ON THE KALMAN FILTER.",
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.",
author = "Valdes, {Juan B} and Velasquez, {Jesus M.} and Ignacio Rodriguez-Iturbe",
year = "1978",
language = "English (US)",
pages = "369--384",
booktitle = "Unknown Host Publication Title",
publisher = "Univ of Pittsburgh, Dep of Civ Eng, Stochastic Hydraul Program",

}

TY - CHAP

T1 - BAYESIAN DISCRIMINATION OF HYDROLOGIC FORECASTING MODELS BASED ON THE KALMAN FILTER.

AU - Valdes, Juan B

AU - Velasquez, Jesus M.

AU - Rodriguez-Iturbe, Ignacio

PY - 1978

Y1 - 1978

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=0018108372&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0018108372&partnerID=8YFLogxK

M3 - Chapter

AN - SCOPUS:0018108372

SP - 369

EP - 384

BT - Unknown Host Publication Title

PB - Univ of Pittsburgh, Dep of Civ Eng, Stochastic Hydraul Program

ER -