Metaheuristic algorithms for groundwater model parameter inversion: Advances and prospects
Junjun Chen, Zhenxue Dai
Abstract
Groundwater inverse modeling is a vital technique for estimating unmeasurable model parameters and enhancing numerical simulation accuracy. This paper comprehensively reviews the current advances and future prospects of metaheuristic algorithm-based groundwater model parameter inversion. Initially, the simulation-optimization parameter estimation framework is introduced, which involves the integration of simulation models with metaheuristic algorithms. The subsequent sections explore the fundamental principles of four widely employed metaheuristic algorithms—genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and differential evolution (DE)—highlighting their recent applications in water resources research and related areas. Then, a solute transport model is designed to illustrate how to apply and evaluate these four optimization algorithms in addressing challenges related to model parameter inversion. Finally, three noteworthy directions are presented to address the common challenges among current studies, including balancing the diverse exploration and centralized exploitation within metaheuristic algorithms, local approximate error of the surrogate model, and the curse of dimensionality in spatial variational heterogeneous parameters. In summary, this review paper provides theoretical insights and practical guidance for further advancements in groundwater inverse modeling studies.