Premium
Parameter Identification of Groundwater Aquifer Models: A Generalized Least Squares Approach
Author(s) -
Sadeghipour Jamshid,
Yeh William WG.
Publication year - 1984
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr020i007p00971
Subject(s) - least squares function approximation , ordinary least squares , reliability (semiconductor) , estimation theory , noise (video) , covariance , trace (psycholinguistics) , mathematics , covariance matrix , statistics , computer science , algorithm , artificial intelligence , power (physics) , physics , linguistics , philosophy , quantum mechanics , estimator , image (mathematics)
This paper concerns the methods of estimating aquifer transmissivities on the basis of unsteady state hydraulic head data. Traditionally, the criterion of minimizing the sum of the squares of errors has been used to match the observed data with the model response. The data used for optimization usually contain noise that is not necessarily uncorrelated. It is well understood that the results of identification methods are very sensitive to measurement errors in data. In this study, the ordinary least squares (OLS) technique is carried out along with a generalized least squares (GLS) technique specifically designed to reduce the effect of correlated errors. The trace of the covariance matrix is used as a measure of overall accuracy and reliability of the estimated parameters. The effectiveness of the OLS and GLS techniques in dealing with noisy data is demonstrated by using a hypothetical example. The results of numerical experiments suggest that GLS offers a promising approach in efficiently improving the reliability of the estimated parameters.