Applications of recursive estimation techniques to time variable hydrological systems
Journal of HydrologyPublished 1 January 1979
P. G. Whitehead
Citations25
SJR quartileQ1
SJR score1.91
SNIP1.67
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
In this paper a flexible methodology is illustrated using the recursive instrumental variable approximate maximum likelihood algorithms of time-series analysis to identify parameter variations and characterise time-varying hydrological systems. The approach is developed for a single-input-single-output rainfall-runoff process and extended to the multivariable situation for a river water quality system.
Keywords
EngineeringEnvironmental Science
Journal of the Environmental Engineering DivisionSystematic Identification of DO-BOD Model Structure
123 Citations1976Bruce Beck, Peter C. Young
Proceedings of the Institution of Electrical EngineersOptimal smoothing in the identification of linear time-varying systems
96 Citations1975J.P. Norton
Water Resources ResearchAn adaptive algorithm for analyzing short‐term structural and parameter changes in hydrologic prediction models
48 Citations1978Eric F. Wood, András Szöllösi‐Nagy
International Journal of ControlA recursive approach to time-series analysis for multi-variable systems
44 Citations1977Peter C. Young, P. G. Whitehead
The recursive instrumental variable-approximate maximum likelihood (IV-AML) method of time-series analysis for single-input, single-output systems is extended to the characterization of multivariable systems using techniques of statistical feature selection to circumvent some of the problems of characterizing multi-variable stochastic disturbances.
AutomaticaReal time recursive prediction of river flows
38 Citations1973R.L. Kashyap, A. R. Rao
The prediction schemes are used to obtain one-day-ahead forecasts of the Wabash River in the U.S.A. by using the daily riverflow data, paying particular attention to the choice of the order of the stochastic difference equation.