AQUIFER PARAMETER-ESTIMATION USING GENETIC ALGORITHMS AND NEURAL NETWORKS

Computational tools such as genetic algorithms and neural networks are becoming increasingly popular in scientific applications involving ma thematical modeling. These tools emulate natural biological processes in an attempt to build more robust and efficient mathematical models. The present study explores the applicability of genetic algorithms and neural networks for aquifer parameter estimation, in an optimization framework. Although optimization models based on genetic algorithms ar e more robust than conventional nonlinear programming techniques, they often necessitate many computationally expensive function evaluations . On the other hand, genetic algorithms can also tolerate approximate function evaluations. The present study employs artificial neural netw orks that provide quick but reasonably accurate function evaluation, i n conjunction with genetic algorithms. Such an optimization framework makes the resulting calibration model highly robust and efficient. App licability of the proposed model is demonstrated on a hypothetical aqu ifer using synthetic test data. Through an extensive sensitivity analy sis, the present study reiterates that a low probability of mutation ( 0.02-0.03) and a moderately high probability of crossover (0.6-0.7) ar e essential for good convergence of a genetic optimization model.