Multiobjective optimization of the groundwater exploitation layout in coastal areas based on multiple surrogate models

Fan, Yongchang; Lu, Wenxi; Miao, Tiansheng; Li, Jiuhui; Jiang, Lin

doi:10.1007/s11356-020-08367-2

Cited by 26 publications

(20 citation statements)

References 56 publications

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“…In summary, the following conclusions can be drawn for all the above test results: (1) Compared to the non-dimensionality reduction Kriging method, regardless of the modeling time and the accuracy of the model, the HDKM-PCDR method and the KPLS method using dimensionality reduction have been improved. (2) The modeling time of the HDKM-PCDR method is almost always shorter than that of the KPLS method while retaining the same number of PCs. Additionally, with the increase in the dimension and the number of sample points, the efficiency advantage of the HDKM-PCDR method becomes more and more obvious.…”

Section: Numerical Testmentioning

confidence: 99%

“…If the Kriging model is used to estimate the variance of point x i , we first need to reconstruct the Kriging model with the remaining k-1 sampling points, except for point x i . Then, calculate the estimated varianceŝ 2 i of point x i by using the newly built Kriging model and Formula (8). After repeating k times to complete the variance estimation of these k sampling points, the average value can be calculated to obtain the RMSE with Equation (12).…”

Section: Numerical Testmentioning

confidence: 99%

“…The surrogate model [1][2][3][4][5], also called a "response surface model", a "meta model", an "approximate model" or a "simulator", has been applied to different engineering design fields. Commonly used surrogate models include PRS (polynomial response surface) [6,7], Kriging [8][9][10][11][12], RBF (radial basis function) [13,14], SVR (support vector regression) [15,16] and MARS (multiple adaptive spline regression).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved High-Dimensional Kriging Surrogate Modeling Method through Principal Component Dimension Reduction

Shi

Yin

et al. 2021

Mathematics

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The Kriging surrogate model in complex simulation problems uses as few expensive objectives as possible to establish a global or local approximate interpolation. However, due to the inversion of the covariance correlation matrix and the solving of Kriging-related parameters, the Kriging approximation process for high-dimensional problems is time consuming and even impossible to construct. For this reason, a high-dimensional Kriging modeling method through principal component dimension reduction (HDKM-PCDR) is proposed by considering the correlation parameters and the design variables of a Kriging model. It uses PCDR to transform a high-dimensional correlation parameter vector in Kriging into low-dimensional one, which is used to reconstruct a new correlation function. In this way, time consumption of correlation parameter optimization and correlation function matrix construction in the Kriging modeling process is greatly reduced. Compared with the original Kriging method and the high-dimensional Kriging modeling method based on partial least squares, the proposed method can achieve faster modeling efficiency under the premise of meeting certain accuracy requirements.

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Section: Numerical Testmentioning

confidence: 99%

Section: Numerical Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved High-Dimensional Kriging Surrogate Modeling Method through Principal Component Dimension Reduction

Shi

Yin

et al. 2021

Mathematics

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“…Surrogate models is a term that encompasses a multitude of simplification‐methods and stand‐ins, with the common goal to “increase computational efficiency” (Asher et al 2015). Usually, they are divided in three distinct categories (cf Asher et al 2015): projection‐based methods (e.g., Vermeulen et al 2004a; McPhee and Yeh 2008; Pasetto et al 2013; Boyce and Yeh 2014; Ushijima and Yeh 2015; Stanko et al 2016), data‐driven methods (e.g., Khu and Werner 2003; Yoon et al 2011; Taormina et al 2012; Roy et al 2016; Fan et al 2020; Yin and Tsai 2020), and structural simplification methods (e.g., Sun 2008; Doherty and Christensen 2011; Watson et al 2013; von Gunten et al 2014; Knowling et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…In the field of data worth analysis for hydro systems, surrogate models have been applied in recent years for optimization of remediation strategies (e.g., Sbai 2019; Yin and Tsai 2020), optimal design of measurement networks (e.g., Sreekanth et al 2017; Sreekanth et al 2020), or general groundwater management/exploitation optimization (e.g., Roy et al 2016; Fan et al 2020). Knowling et al (2020) used structurally simplified surrogates in combination with a full benchmark model to estimate data worth and the effects of data on prediction confidence.…”

Section: Introductionmentioning

confidence: 99%

Robust Data Worth Analysis with Surrogate Models

Gosses

Wöhling

2021

Groundwater

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Highly detailed physically based groundwater models are often applied to make predictions of system states under unknown forcing. The required analysis of uncertainty is often unfeasible due to the high computational demand. We combine two possible solution strategies: (1) the use of faster surrogate models; and (2) a robust data worth analysis combining quick first‐order second‐moment uncertainty quantification with null‐space Monte Carlo techniques to account for parametric uncertainty. A structurally and parametrically simplified model and a proper orthogonal decomposition (POD) surrogate are investigated. Data worth estimations by both surrogates are compared against estimates by a complex MODFLOW benchmark model of an aquifer in New Zealand. Data worth is defined as the change in post‐calibration predictive uncertainty of groundwater head, river‐groundwater exchange flux, and drain flux data, compared to the calibrated model. It incorporates existing observations, potential new measurements of system states (“additional” data) as well as knowledge of model parameters (“parametric” data). The data worth analysis is extended to account for non‐uniqueness of model parameters by null‐space Monte Carlo sampling. Data worth estimates of the surrogates and the benchmark suggest good agreement for both surrogates in estimating worth of existing data. The structural simplification surrogate only partially reproduces the worth of “additional” data and is unable to estimate “parametric” data, while the POD model is in agreement with the complex benchmark for both “additional” and “parametric” data. The variance of the POD data worth estimates suggests the need to account for parameter non‐uniqueness, like presented here, for robust results.

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Multi-objective Optimization of Curtain Grouting Construction Scheme with Ensemble Residual Surrogate Model

Zhang,

Wang,

et al. 2024

Rock Mech Rock Eng

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Multiobjective optimization of the groundwater exploitation layout in coastal areas based on multiple surrogate models

Cited by 26 publications

References 56 publications

An Improved High-Dimensional Kriging Surrogate Modeling Method through Principal Component Dimension Reduction

An Improved High-Dimensional Kriging Surrogate Modeling Method through Principal Component Dimension Reduction

Robust Data Worth Analysis with Surrogate Models

Multi-objective Optimization of Curtain Grouting Construction Scheme with Ensemble Residual Surrogate Model

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