Pumping Optimization of Coastal Aquifers Assisted by Adaptive Metamodelling Methods and Radial Basis Functions

• Published in: Water resources management 2016-12, Vol.30 (15), p.5845-5859
• Main Authors: Christelis, Vasileios, Mantoglou, Aristotelis
• Format: Article
• Language: English
• Subjects: Approximation; Aquifer management; Aquifers; Atmospheric Sciences; Civil Engineering; Coastal aquifers; Computation; Computer simulation; Earth and Environmental Science; Earth Sciences; Engineering; Environment; Evolution; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology/Water Resources; Mathematical models; Methods; Neural networks; Optimization; Parameter estimation; Performance assessment; Problems; Pumping; Saline water intrusion; Seawater; Simulation; Strategy; Water resources
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-016-1337-3

The application of metamodelling frameworks is a popular approach to handle the computational cost arising from complex computer simulations and global optimization algorithms in simulation-optimization routines. In this paper, Radial Basis Functions (RBF) are used as metamodels for the computationally expensive variable-density flow and salt transport numerical simulations, in a pumping optimization problem of coastal aquifers. While RBF metamodels have been fairly utilized in many engineering optimization problems, their use is very limited in coastal aquifer management. Two adaptive metamodelling frameworks are employed, that is, the adaptive-recursive approach and the metamodel-embedded evolution strategy. In both frameworks, cubic RBF models are used to approximate the constraint functions imposed on the coastal aquifer pumping optimization problem. The optimal pumping rates are first calculated based on the variable-density and salt transport numerical models of seawater intrusion. The resulting optimal solutions and the computational times are set as benchmark values in order to assess the performance of the metamodelling optimization strategies. Results indicate that the metamodel-embedded evolution framework outperformed in terms of computational efficiency the adaptive-recursive approach while it successfully located the region of the global optimum. Furthermore, with the metamodel-embedded evolution strategy the computational time of the variable-density-based optimization was reduced by 96 %.