Parameter estimation of an ARMA model for river flow forecasting using goal programming

River flow forecasting constitutes one of the most important applications in hydrology. Several methods have been developed for this purpose and one of the most famous techniques is the Auto regressive moving average (ARMA) model. In the research reported here, the goal was to minimize the error for a specific season of the year as well as for the complete series. Goal programming (GP) was used to estimate the ARMA model parameters. Shaloo Bridge station on the Karun River with 68 years of observed stream flow data was selected to evaluate the performance of the proposed method. The results when compared with the usual method of maximum likelihood estimation were favorable with respect to the new proposed algorithm.

[1]  J. Sprave.  LINEAR NEIGHBORHOOD EVOLUTION STRATEGY , 1999 .

[2]  R. Abrahart,et al.  Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments , 2000 .

[3]  Zülal Güngör,et al.  An application of fuzzy goal programming to a multiobjective project network problem , 2001, Fuzzy Sets Syst..

[4]  A. Charnes,et al.  Management Models and Industrial Applications of Linear Programming , 1961 .

[5]  Abraham Charnes,et al.  Management Models and Industrial Applications of Linear Programming , 1961 .

[6]  Frederick S. Hillier,et al.  Introduction of Operations Research , 1967 .

[7]  S. M. Junaid Zaidi,et al.  Linear Programming in Single and Multiple Objective Systems , 1982 .

[8]  Andrew Harvey,et al.  An Algorithm for Exact Maximum Likelihood Estimation of Autoregressive–Moving Average Models by Means of Kaiman Filtering , 1980 .

[9]  D. A. Conway.  Management Goals and Accounting for Control , 1966 .

[10]  Rajae Azrak,et al.  The exact quasi-likelihood of time dependent ARMA models , 1998 .

[11]  E. Toth,et al.  Comparison of short-term rainfall prediction models for real-time flood forecasting , 2000 .

[12]  Frederick S. Hillier,et al.  Introduction of Operations Research , 1967 .

[13]  H. Brian Hwarng,et al.  Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures , 2001 .

[14]  G. Box,et al.  The likelihood function of stationary autoregressive-moving average models , 1979 .

[15]  Robert D. St. Louis,et al.  Automatic ARMA identification using neural networks and the extended sample autocorrelation function: a reevaluation , 2000, Decis. Support Syst..

[16]  Guy Melard,et al.  Algorithm AS197: A fast algorithm for the exact likelihood of autoregressive-moving average models , 1984 .

[17]  L. R. Amey.  Management Goals and Accounting for Control , 1966 .

[18]  J. Pearlman.  An algorithm for the exact likelihood of a high-order autoregressive-moving average process , 1980 .

[19]  C. Ansley.  An algorithm for the exact likelihood of a mixed autoregressive-moving average process , 1979 .

[20]  James P. Ignizio,et al.  Linear Programming in Single- and Multiple-Objective Systems , 1984 .