Application of genetic programming models incorporated in optimization models for contaminated groundwater systems management

Abstract

Two different applications of Genetic Programming (GP) for solving large scale groundwater management problems are presented here. Efficient groundwater contamination management needs solution of large sale simulation models as well as solution of complex optimal decision models. Often the best approach is to use linked simulation optimization models. However, the integration of optimization algorithm with large scale simulation of the physical processes, which require very large number of iterations, impose enormous computational burden. Often typical solutions need weeks of computer time. Suitably trained GP based surrogate models approximating the physical processes can improve the computational efficiency enormously, also ensuring reasonably accurate solutions. Also, the impact factors obtained from the GP models can help in the design of monitoring networks under uncertainties. Applications of GP for obtaining impact factors implicitly based on a surrogate GP model, showing the importance of a chosen monitoring location relative to a potential contaminant source is also presented. The first application utilizes GP models based impact factors for optimal design of monitoring networks for efficient identification of unknown contaminant sources. The second application utilizes GP based ensemble surrogate models within a linked simulation optimization model for optimal management of saltwater intrusion in coastal aquifers.