Computational cost is a critical issue for large-scale water resource optimization problems that often involve time-consuming simulation models. Less accurate approximation ("meta") models can be used to improve computational efficiency. We propose a novel trust-region-based metamodel framework, in which hierarchically trained metamodels are embedded into a genetic algorithm (GA) optimization framework to replace time-consuming numerical models. Numerical solutions produced from early generations of the GA, along with solutions dynamically sampled from later generations, are used to retrain the metamodels and correct the GA's converging route. A bootstrap sampling technique is used to cluster the collected numerical solutions into hierarchical training regions and then multiple metamodels are trained based on these clustered regions. The hierarchically trained metamodels are then used to approximate the numerical models. A trust region testing strategy selects the most appropriate metamodels for prediction. This allows the local regions (particularly those near the optimal solution) to be approximated by smoother and smaller metamodels with higher accuracy. This can speed up GA's convergence when the population moves into local regions. The technique was tested with artificial neural networks (ANNs) and support vector machines (SVMs) on a field-scale groundwater remediation case in a distributed network computation environment. Our preliminary results show that the adaptive meta-model GA (AMGA) with the trust region based training technique converges with higher accuracy with the same computation effort. Copyright ASCE 2005.