Adaptive Preconditioning Krylov Subspace Methods for Efficient Numerical Groundwater Flow Modeling in Steady-State Conditions

The numerical method plays an important role in groundwater flow modeling to solve linear equations with sparse matrices. This study evaluates the performance of the Krylov Subspace method with adaptive preconditioning compared to classical iterative methods, such as Gauss-Seidel, Jacobi, and Successive Over-Relaxation (SOR), in the simulation of steady-state groundwater flow on a 2D grid. The results show that the Krylov method with adaptive preconditioning provides the fastest execution time (0.0054 seconds) with minimal resource usage, such as CPU of 0.0% and RAM of only 0.175 MB. In contrast, the classic iterative method shows longer execution times and greater resource consumption. This study concludes that the Krylov Subspace method with adaptive preconditioning is the best solution for applications that require high efficiency in groundwater flow computing