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 mathematical 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 are 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 networks that provide quick but reasonably accurate function evaluation, in conjunction with genetic algorithms. Such an optimization framework makes the resulting calibration model highly robust and efficient. Applicability of the proposed model is demonstrated on a hypothetical aquifer using synthetic test data. Through an extensive sensitivity analysis, 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) are essential for good convergence of a genetic optimization model.