Using a hybrid approach to optimize experimental network design for aquifer parameter identification

This research develops an optimum design model of groundwater network using genetic algorithm (GA) and modified Newton approach, based on the experimental design conception. The goal of experiment design is to minimize parameter uncertainty, represented by the covariance matrix determinant of estimated parameters. The design problem is constrained by a specified cost and solved by GA and a parameter identification model. The latter estimates optimum parameter value and its associated sensitivity matrices. The general problem is simplified into two classes of network design problems: an observation network design problem and a pumping network design problem. Results explore the relationship between the experimental design and the physical processes. The proposed model provides an alternative to solve optimization problems for groundwater experimental design.

[1]  E. Sidiropoulos,et al.  Well Locations and Constraint Handling in Groundwater Pumping Cost Minimization via Genetic Algorithms , 2004 .

[2]  Tracy Nishikawa,et al.  Optimal pumping test design for the parameter identification of groundwater systems , 1989 .

[3]  Roko Andričević,et al.  Coupled withdrawal and sampling designs for groundwater supply models , 1993 .

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

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

[6]  Dennis Cooke,et al.  Optimal Design: An Introduction to the Theory for Parameter Estimation , 1981 .

[7]  Frank T.-C. Tsai,et al.  Characterization and identification of aquifer heterogeneity with generalized parameterization and Bayesian estimation , 2004 .

[8]  P. Patrick Wang,et al.  Parameter structure identification using tabu search and simulated annealing , 1996 .

[9]  Arlen W. Harbaugh,et al.  A modular three-dimensional finite-difference ground-water flow model , 1984 .

[10]  E. Poeter,et al.  Documentation of UCODE; a computer code for universal inverse modeling , 1998 .

[11]  Andres Alcolea,et al.  Inverse problem in hydrogeology , 2005 .

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

[13]  Ching-Pin Tung,et al.  Pattern classification using tabu search to identify the spatial distribution of groundwater pumping , 2004 .

[14]  Roko Andričević,et al.  A Real‐Time Approach to Management and Monitoring of Groundwater Hydraulics , 1990 .

[15]  W. Yeh,et al.  Optimal observation network design for parameter structure identification in groundwater modeling , 2005 .

[16]  Liang-Cheng Chang,et al.  Optimal planning of a dynamic pump-treat-inject groundwater remediation system , 2007 .

[17]  S. Ranji Ranjithan,et al.  A HYBRID OPTIMIZATION APPROACH TO THE ESTIMATION OF DISTRIBUTED PARAMETERS IN TWO‐DIMENSIONAL CONFINED AQUIFERS 1 , 1998 .

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

[19]  Thomas C. Harmon,et al.  Experimental design and model parameter estimation for locating a dissolving dense nonaqueous phase liquid pool in groundwater , 2002 .

[20]  T. Culver,et al.  Constraint Handling for Genetic Algorithms in Optimal Remediation Design , 2000 .

[21]  C. Zheng,et al.  GROUND WATER MANAGEMENT OPTIMIZATION USING GENETIC ALGORITHMS AND SIMULATED ANNEALING: FORMULATION AND COMPARISON 1 , 1998 .

[22]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[23]  Arlen W. Harbaugh,et al.  MODFLOW-2000, The U.S. Geological Survey Modular Ground-Water Model - User Guide to Modularization Concepts and the Ground-Water Flow Process , 2000 .

[24]  F. Tsai,et al.  A Combinatorial Optimization Scheme for Parameter Structure Identification in Ground Water Modeling , 2003, Ground water.

[25]  Nien-Sheng Hsu,et al.  Optimum experimental design for parameter identification in groundwater hydrology , 1989 .