The Groundwater and the Groundwater Quality Management Problem: Reliability and Solution Techniques

The groundwater and the groundwater quality management models are defined as an engineering decision problem. The solution of this problem is not trivial and can be reached with the use of deterministic or stochastic techniques. The determistic approaches (Primal Method) compute only local minima, or the global minimum (Outer Approximation Methods) only according to strict hypothesis. The stochastic methods (Simulated Annealing, Genetic Algorithms or Neural Networks) find the global minimum, but only in statistical sense. The most recent approaches to the problem treat the physical parameters (transmissivity) as a random field. The uncertainty of the transmissivity makes impossible to guarantee the feasibility of the optimal strategy. A reliability level is defined as the ratio between the number of realizations that mantain feasible the optimal solution out of the total number of realizations. Some of the algorithms proposed to reach a fixed reliability are discussed. The idea of linking the goal of the management problem with the number and location of new field measurements is introduced.

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

[2]  J. Eheart,et al.  Neural network-based screening for groundwater reclamation under uncertainty , 1993 .

[3]  G. Pinder,et al.  Groundwater management using numerical simulation and the outer approximation method for global optimization , 1993 .

[4]  James W. Male,et al.  Model for Prescribing Ground‐Water Use Permits , 1992 .

[5]  William W.-G. Yeh,et al.  Optimal pumping test design for parameter estimation and prediction in groundwater hydrology , 1990 .

[6]  S. Gorelick,et al.  Reliable aquifer remediation in the presence of spatially variable hydraulic conductivity: From data to design , 1989 .

[7]  G. Pinder,et al.  Groundwater Quality Management Using Numerical Simulation and a Primal Optimization Technique , 1994 .

[8]  Hamid R. Nemati,et al.  Groundwater quality management under uncertainty: stochastic programming approaches and the value of information , 1992 .

[9]  Thomas Maddock,et al.  Management model as a tool for studying the worth of data , 1973 .

[10]  C. Tiedeman,et al.  Analysis of uncertainty in optimal groundwater contaminant capture design , 1993 .

[11]  S. Gorelick,et al.  Optimal groundwater quality management under parameter uncertainty , 1987 .

[12]  C. Shoemaker,et al.  Dynamic optimal control for groundwater remediation with flexible management periods , 1992 .

[13]  E. Wood,et al.  A distributed parameter approach for evaluating the accuracy of groundwater model predictions: 1. Theory , 1988 .

[14]  G. Gambolati,et al.  Optimal dewatering schemes in the foundation design of an electronuclear plant , 1988 .

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

[16]  Dennis McLaughlin,et al.  A distributed parameter approach for evaluating the accuracy of groundwater model predictions: 2. Application to groundwater flow , 1988 .

[17]  David M. Nielsen,et al.  AQUIFER RESTORATION AND GROUND‐WATER REHABILITATION‐A LIGHT AT THE END OF THE TUNNEL , 1982 .

[18]  J. Eheart,et al.  Aquifer remediation design under uncertainty using a new chance constrained programming technique , 1993 .

[19]  G. Pinder,et al.  Optimal data acquisition strategy for the development of a transport model for groundwater remediation , 1991 .