Parameter structure identification using tabu search and simulated annealing

In groundwater modeling the identification of an optimal flow or transport parameter that varies spatially should include both the values and structure of the parameter. However, most existing techniques for parameter identification only consider the parameter values. In this study, the problem of identifying optimal parameter structure is treated as a large combinatorial optimization problem. Two recently developed heuristic search techniques, simulated annealing and tabu search, are used to solve the large combinatorial optimization problem. The effectiveness and flexibility of these two techniques are evaluated and compared with simple grid search and descent search, using preliminary results from one-dimensional examples. Among the techniques examined in this paper, tabu search performs extremely well in terms of the total number of function evaluations required.

[1]  Shah Shah,et al.  Error Analysis in History Matching: The Optimum Level of Parameterization , 1978 .

[2]  W. Yeh Review of Parameter Identification Procedures in Groundwater Hydrology: The Inverse Problem , 1986 .

[3]  William W.-G. Yeh,et al.  Aquifer parameter identification with optimum dimension in parameterization , 1981 .

[4]  K. H. Coats,et al.  A New Technique for Determining Reservoir Description from Field Performance Data , 1970 .

[5]  J. C. Ramírez,et al.  Estimation of aquifer parameters under transient and steady-state conditions , 1984 .

[6]  W. Yeh,et al.  Sequential estimation of aquifer parameters , 1988 .

[7]  Dan Rosbjerg,et al.  A Comparison of Four Inverse Approaches to Groundwater Flow and Transport Parameter Identification , 1991 .

[8]  S. Ranjithan,et al.  Using genetic algorithms to solve a multiple objective groundwater pollution containment problem , 1994 .

[9]  Antoine Mensch,et al.  An inverse technique for developing models for fluid flow in fracture systems using simulated annealing , 1993 .

[10]  S. P. Neuman,et al.  Effects of kriging and inverse modeling on conditional simulation of the Avra Valley Aquifer in southern Arizona , 1982 .

[11]  B. Noble Applied Linear Algebra , 1969 .

[12]  Irfan A. Khan,et al.  Inverse Problem in Ground Water: Model Development , 1986 .

[13]  David G. Luenberger,et al.  Linear and nonlinear programming , 1984 .

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  William W.-G. Yeh,et al.  A stochastic inverse solution for transient groundwater flow: Parameter identification and reliability analysis , 1992 .

[16]  S. Yakowitz,et al.  A direct method for the identification of the parameters of dynamic nonhomogeneous aquifers , 1975 .

[17]  Probability of plume interception using conditional simulation of hydraulic head and inverse modeling , 1991 .

[18]  Richard L. Cooley,et al.  A method of estimating parameters and assessing reliability for models of steady state Groundwater flow: 2. Application of statistical analysis , 1979 .

[19]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[20]  D. McKinney,et al.  Genetic algorithm solution of groundwater management models , 1994 .

[21]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[22]  R. Zhang,et al.  Applied Contaminant Transport Modeling: Theory and Practice , 1991 .

[23]  G. Marsily,et al.  An Automatic Solution for the Inverse Problem , 1971 .

[24]  W. Yeh,et al.  Identification of Parameter Structure in Groundwater Inverse Problem , 1985 .

[25]  Robert A. Marryott,et al.  Optimal Groundwater Management: 1. Simulated Annealing , 1991 .

[26]  David E. Dougherty,et al.  Optimal groundwater management: 2. Application of simulated annealing to a field-scale contamination site , 1993 .

[27]  William W.-G. Yeh,et al.  Aquifer parameter identification with kriging and optimum parameterization , 1983 .

[28]  M. Hill A computer program (MODFLOWP) for estimating parameters of a transient, three-dimensional ground-water flow model using nonlinear regression , 1992 .

[29]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[30]  Akhil Datta-Gupta,et al.  Stochastic Reservoir Modeling Using Simulated Annealing and Genetic Algorithm , 1995 .

[31]  S. P. Neuman,et al.  Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information , 1986 .

[32]  J. P. Delhomme,et al.  Spatial variability and uncertainty in groundwater flow parameters: A geostatistical approach , 1979 .