Aquifer flow parameter estimation using coupled meshless methods and metaheuristic algorithms

A reliable analysis of various groundwater problems requires accurate input of aquifer parameters. However, field measurement of such parameters is tedious and expensive. Inverse modelling by Simulation-Optimization (SO) resolves this limitation. In this study, the unknown transmissivity of confined...

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Veröffentlicht in:Environmental modelling & software : with environment data news Jg. 177; S. 106050
Hauptverfasser: Das, Sanjukta, Eldho, T.I.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.06.2024
Schlagworte:
algorithms
Aquifer parameter estimation
aquifers
computer software
groundwater
Groundwater flow
Meshless model
Meshless weak strong
Simulation optimization model
Whale optimization algorithm
Meshless weak strong
Whale optimization algorithm
Simulation optimization model
Meshless model
Groundwater flow
Aquifer parameter estimation
ISSN:1364-8152, 1873-6726
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Zusammenfassung:A reliable analysis of various groundwater problems requires accurate input of aquifer parameters. However, field measurement of such parameters is tedious and expensive. Inverse modelling by Simulation-Optimization (SO) resolves this limitation. In this study, the unknown transmissivity of confined aquifers is estimated using SO models. Three simulation models of strong, weak and hybrid categories of meshless methods, namely, Radial Point Collocation Method (RPCM), Meshless Local Petrov Galerkin (MLPG) and Meshless Weak Strong (MWS) form, are coupled with metaheuristic algorithms of Differential Evolution (DE), Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA), resulting in nine SO models. Five of these nine models are novel SO models and first-time application of WOA for groundwater flow parameter estimation. The application of models to heterogeneous hypothetical and complex field-type aquifers prove that solutions are similar to true transmissivities. This study provides an insight into the selection of suitable SO model based on available resources and requirements. •Nine Simulation-Optimization (SO) models are presented for transmissivity estimation.•The flow simulations are based on meshless weak, strong and hybrid form methods.•Simulations are coupled with 3 metaheuristic algorithms and SO models are compared.•The relative advantages of each model and the choice of selection is discussed.
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ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2024.106050