Kriging-approximation Simulated Annealing Algorithm for Network-based Porous Media Flow Modeling Optimization

Optimization algorithms are often applied to search best parameters for complex groundwater models. Running the complex groundwater models to evaluate objective function might be time-consuming. This research proposes a Kriging-approximation simulated annealing algorithm. Kriging is a spatial statistics method used to interpolate unknown variables based on surrounding given data. In the algorithm, Kriging method is used to estimate complicate objective function and is incorporated with simulated annealing. The contribution of the Kriging-approximation simulated annealing algorithm is to reduce calculation time and increase efficiency. In this research, we build a network-based porous media flow model. Observe how fluid flows through pores of soil body and achieves the entire flow condition. The size of unknown variables make problem complicated. Kriging-approximation simulated annealing algorithm is applied to optimize the complex model optimization problem. With the incorporated algorithm, it is able to solve the problem more efficiently. By adjusting the parameter in this model, make the network-based porous media flow model more realistic.