Metaheuristic algorithms for groundwater model parameter inversion:Advances and prospects  

作　　者：Junjun Chen Zhenxue Dai 

机构地区：[1]National and Local Joint Engineering Laboratory of Internet Application Technology on Mine,China University of Mining and Technology,Xuzhou 221116,China [2]College of Construction Engineering,Jilin University,Changchun 130026,China

出　　处：《Deep Resources Engineering》2024年第2期101-108,共8页深部资源工程（英文）

基　　金：supported by the Fundamental Research Funds for the Central Universities(XJ2023005201);the National Natural Science Foundation of China(NSFC:U2267217,42141011,and 42002254).

摘　　要：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 approxi-mate error of the surrogate model,and the curse of dimensionality in spatial variational heterogeneous pa-rameters.In summary,this review paper provides theoretical insights and practical guidance for further advancements in groundwater inverse modeling studies.

关 键 词：Groundwater Inverse modeling Metaheuristic algorithms Genetic algorithm Particle swarm optimization Simulated annealing Differential evolution 

分 类 号：P64[天文地球—地质矿产勘探]