OPTIMAL DATA ACQUISITION STRATEGY FOR THE DEVELOPMENT OF A TRANSPORT MODEL FOR GROUNDWATER REMEDIATION

The reliability of groundwater quality management algorithms is limited in large part by the uncertainty present in the model parameters. Because the field parameter measurement costs and the remediation costs must be supplied by the same financial source, the classical optimization procedure does not minimize the real total remediation investment. This research presents an algorithm able to find the total minimum for the sum of both the measurement and the pumping costs. A chance-constrained technique is used to cast the optimization problem in stochastic form, relating the concentration covariance matrix to the log-transmissivity covariance matrix by means of the transport equations and a first-order approximation for the uncertainty. The simulation model solves the steady state flow equations on a finite element triangular mesh and the transport equations using the backward method of characteristics. The resulting nonlinearly constrained optimization problem is solved using the quasi-linearity algorithm; this algorithm is designed to find a good initial point for the local minimum search when the feasible domain is not convex.