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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Bibliographic Details
Published in:Environmental modelling & software : with environment data news Vol. 177; p. 106050
Main Authors: Das, Sanjukta, Eldho, T.I.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.06.2024
Subjects:
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
Online Access:Get full text
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Summary: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