Surrogate-based pumping optimization of coastal aquifers under limited computational budgets

The computationally expensive variable density and salt transport numerical models hinder the implementation of simulation-optimization routines for coastal aquifer management. To reduce the computational cost, surrogate models have been utilized in pumping optimization of coastal aquifers. However,...

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Bibliographic Details
Published in:Journal of hydroinformatics 2018-01, Vol.20 (1), p.164-176
Main Authors: Christelis, Vasileios, Regis, Rommel G., Mantoglou, Aristotelis
Format: Article
Language:English
Subjects:
Algorithms
Aquifer management
Aquifers
Basis functions
Budgeting
Budgets
Chemical analysis
Coastal aquifers
Coastal management
Computer applications
Computer simulation
Intrusion
Mathematical models
Modelling
Neural networks
Optimization
Problems
Pumping
Radial basis function
Saline water intrusion
Salt advection
Seawater
Statistical analysis
Statistical methods
Water analysis
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Summary:The computationally expensive variable density and salt transport numerical models hinder the implementation of simulation-optimization routines for coastal aquifer management. To reduce the computational cost, surrogate models have been utilized in pumping optimization of coastal aquifers. However, it has not been previously addressed whether surrogate modelling is effective given a limited number of numerical simulations with the seawater intrusion model. To that end, two surrogate-based optimization (SBO) frameworks are employed and compared against the direct optimization approach, under restricted computational budgets. The first, a surrogate-assisted algorithm, employs a strategy which aims at a fast local improvement of the surrogate model around optimal values. The other, balances global and local improvement of the surrogate model and is applied for the first time in coastal aquifer management. The performance of the algorithms is investigated for optimization problems of moderate and large dimensionalities. The statistical analysis indicates that for the specified computational budgets, the sample means of the SBO methods are statistically significantly better than those of the direct optimization. Additionally, the selection of cubic radial basis functions as surrogate models, enables the construction of very fast approximations for problems with up to 40 decision variables and 40 constraint functions.
ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2017.063