Flow and transport parameter estimation of a confined aquifer using simulation–optimization model

In this study, the simulation–optimization (SO) model is used to identify the aquifer parameters (flow and transport parameters) of a confined aquifer. The unknown parameters are obtained by comparing the observed and the simulated values. The meshless local radial point interpolation method (LRPIM)...

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Bibliographic Details
Published in:Modeling earth systems and environment 2024-06, Vol.10 (3), p.4013-4026
Main Authors: Swetha, K., Eldho, T. I., Singh, L. Guneshwor, Kumar, A. Vinod
Format: Article
Language:English
Subjects:
Aquifers
Chemistry and Earth Sciences
Computer Science
Confined aquifers
Contaminants
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Ecosystems
Environment
Error analysis
Groundwater
Groundwater flow
Math. Appl. in Environmental Science
Mathematical Applications in the Physical Sciences
Optimization
Optimization models
Original Article
Parameter estimation
Parameter identification
Parameters
Particle swarm optimization
Physics
Pollution transport
Simulation
Simulation models
Statistics for Engineering
Transmissivity
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Summary:In this study, the simulation–optimization (SO) model is used to identify the aquifer parameters (flow and transport parameters) of a confined aquifer. The unknown parameters are obtained by comparing the observed and the simulated values. The meshless local radial point interpolation method (LRPIM) is used for the purpose of simulation of groundwater flow/contaminant transport. An optimization model is used to minimize the error between simulated and predetermined head/concentration values. Teaching Learning-Based Optimization (TLBO) is coupled with the LRPIM simulation model to get the SO model (LRPIM-TLBO). Further with Particle Swarm Optimization (PSO), the LRPIM-PSO model is also developed for comparison purpose. The proposed SO model is applied to a hypothetical and real field problem to estimate the aquifer parameters such as transmissivity, longitudinal and transverse dispersivity. The model performance is measured with RMS error. It is found that the RMS error is less than 7 and 10 for hypothetical and real field cases, showing the effectiveness of the SO models for parameter estimation.
ISSN:2363-6203
2363-6211
DOI:10.1007/s40808-024-01989-2