Performance comparison of multiple and single surrogate models for pumping optimization of coastal aquifers

Christelis, Vasileios; Kopsiaftis, George; Mantoglou, Aristotelis

doi:10.1080/02626667.2019.1584400

Cited by 29 publications

(16 citation statements)

References 58 publications

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“…As indicated in Christelis and Mantoglou (2017) conclusion which that emphasized the minor differences between GP and ANN in the simulationoptimization system. The high correlation coefficient of GA among single MMs confirms the previous results of Kourakos and Mantoglou [10] especially in multi-objective optimization formulations.…”

Section: Performance Of Mmsmentioning

confidence: 93%

“…Nevertheless, to converge the optimal pumping patterns, the repetitive process of simulation-optimization (SO) model for computing state variables is time consuming [ 3 ]. One of the techniques to reduce the computational time on the coupled SO model is to construct efficient replacement models, generally named MMs of the original numerical model [ 4 ]. MMs have been applied in recent studies to approximate complex numerical density dependent flow and contaminant transport [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Development of a robust ensemble meta-model for prediction of salinity time series under uncertainty (case study: Talar aquifer)

Ranjbar

Cherubini

2020

Heliyon

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The aim of this study is to develop an accurate and reliable numerical model of the coastal Talar aquifer threatened by seawater intrusion by developing an ensemble meta-model (MM). In comparison with previous methodologies, the developed model has the following superiority: (1) Its performance is enhanced by developing ensemble MMs using four different meta-modelling frameworks, i.e., artificial neural network, support vector regression, radial basis function, genetic programing and evolutionary polynomial regression; (2) The accuracy of different MMs based on 16 integration of four meta-modeling frameworks is compared; and (3) the effect of aquifer heterogeneity on the MM. The performance of the proposed MM was assessed using an illustrative case aquifer subject to seawater intrusion. The obtained results indicate that the ensemble MM that combines all four meta-modeling frameworks outperformed the GP and ANN models, with a correlation coefficient of 0.98. Moreover, the proposed MM using nonlinear-learning ensemble of SVR-EPR achieves a better and robust forecasting performance. Therefore, it can be considered as an accurate and robust simulator to predict salinity levels under different abstraction patterns in variable density flow. The result of uncertainty analyses reveals that robustness value and pumping rate are inversely proportional and scenarios with a robustness measure of about 12% are more reliable.

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Section: Performance Of Mmsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Development of a robust ensemble meta-model for prediction of salinity time series under uncertainty (case study: Talar aquifer)

Ranjbar

Cherubini

2020

Heliyon

View full text Add to dashboard Cite

show abstract

“…Instead, in this comparison, we additionally add three online DDEAs that have been examined on real-world problems. They are EAS-SM3, EAS-SM5, and EAS-SM12, which are the best three algorithms among 12 algorithms on a realworld application optimization [32]. Both the EAS-SM3 and EAS-SM5 employ a single surrogate, respectively, based on a cubic RBFNN and a Kriging model with Gaussian correlation and first-order polynomial, while the EAS-SM12 adopts the ensemble surrogates with optimal weights.…”

Section: J Aerodynamic Airfoil Design Optimizationmentioning

confidence: 99%

“…In addition, when given a set of prebuilt homogeneous or heterogeneous surrogates, better models can be generated through managing and combining prebuilt surrogates properly [29], [30]. However, some studies also show that ensemble surrogates that are more efficient than a single surrogate in theoretical (mathematical functions) problems may not always work better in real-world application problems because the nature of each optimization problem may favor different surrogates [31]- [34]. Therefore, further and more intelligent surrogate ensemble methods are needed to be researched and studied.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates

Zhan

Wang

et al. 2021

IEEE Trans. Cybern.

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Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access. However, the performance of DDEAs relies on their surrogate quality and often deteriorates if the amount of available data decreases. To solve these problems, this article proposes a new DDEA framework with perturbation-based ensemble surrogates (DDEA-PES), which contain two efficient mechanisms. The first is a diverse surrogate generation method that can generate diverse surrogates through performing data perturbations on the available data. The second is a selective ensemble method that selects some of the prebuilt surrogates to form a final ensemble surrogate model. By combining these two mechanisms, the proposed DDEA-PES framework has three advantages, including larger data quantity, better data utilization, and higher surrogate accuracy. To validate the effectiveness of the proposed framework, this article provides both theoretical and experimental analyses. For the experimental comparisons, a specific DDEA-PES algorithm is developed as an instance by adopting a genetic algorithm as the optimizer and radial basis function neural networks as the base models. The experimental results on widely used benchmarks and an aerodynamic airfoil design real-world optimization problem show that the proposed DDEA-PES algorithm outperforms some state-of-the-art DDEAs. Moreover, when compared with traditional nondata-driven methods, the proposed DDEA-PES algorithm only requires about 2% computational budgets to produce competitive results.

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“…Another approach, less common within environmental modelling, is the use of physics-based metamodels which are constructed by combining models of different fidelity (e.g. Bianchi et al, 2015;Christelis and Mantoglou, 2017).…”

Section: Introductionmentioning

confidence: 99%

Metamodel-assisted analysis of an integrated model composition: An example using linked surface water – groundwater models

Christelis

Hughes

2018

Environmental Modelling & Software

Self Cite

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Highlights 1) Distributed groundwater models were linked together with a river model via the OpenMI software platform. 2) Kriging metamodels were applied to facilitate analysis with the integrated models. 3) The overall computational savings were in the range of 70-90%. 4) Monte Carlo simulations demonstrate that metamodels were in good agreement with the responses of the integrated models. 5) Sensitivity Analysis using the metamodels accurately identified the important parameters.

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Performance comparison of multiple and single surrogate models for pumping optimization of coastal aquifers

Cited by 29 publications

References 58 publications

Development of a robust ensemble meta-model for prediction of salinity time series under uncertainty (case study: Talar aquifer)

Development of a robust ensemble meta-model for prediction of salinity time series under uncertainty (case study: Talar aquifer)

Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates

Metamodel-assisted analysis of an integrated model composition: An example using linked surface water – groundwater models

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