Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites

Hou, Zeyu; Lu, Wenxi

doi:10.1007/s10040-017-1690-1

Cited by 47 publications

(7 citation statements)

References 38 publications

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“…KELM was used to generalize extreme learning machine (ELM) by transforming the explicit activation function to an implicit mapping function (Chen et al, 2014;Shi et al, 2014;Hou and Lu, 2018a).…”

Section: Kelm Methodsmentioning

confidence: 99%

Application of Mixed-Integer Nonlinear Optimization Programming Based on Ensemble Surrogate Model for Dense Nonaqueous Phase Liquid Source Identification in Groundwater

HouZeyu

DaiZhenxue

LaoWangmei

et al. 2019

Environmental Engineering Science

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Groundwater contamination source identification (GCSI) is critical for taking effective measures to protect groundwater resources, assess risks, mitigate disasters, and design remediation strategies. Simulation-optimization techniques have been effective tools for GCSI. However, previous studies have applied individual surrogate models when replacing simulation models, rather than making efforts to combine various methods to improve the approximation accuracy of the surrogate model over the simulation model. In this study, the kernel extreme learning machine (KELM) model was proposed to enhance the surrogate model, and to approach GCSI problems, especially those of dense nonaqueous phase liquid-contaminated aquifers, more effectively. In addition, a kriging model and a support vector regression (SVR) model were built and compared with the KELM model, and various ensemble surrogate (ES) modeling techniques were applied to establish four ES models. Results showed that the KELM model was more accurate than the kriging and SVR models; however, the ES models performed much better than the three individual surrogate models. The most precise ES model increased the certainty coefficient (R 2 ) to 0.9837, whereas limiting the maximum relative error to 13.14%. Finally, a mixedinteger nonlinear programming optimization model was established to identify the groundwater contamination source in terms of location and release history, and simultaneously assess aquifer parameters. The ES model developed in this article could reasonably predict the system response under given operation conditions. Replacement of the simulation model by the ES model considerably reduced the computation burden of the simulation-optimization process and simultaneously achieved high computation accuracy.

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Section: Kelm Methodsmentioning

confidence: 99%

Application of Mixed-Integer Nonlinear Optimization Programming Based on Ensemble Surrogate Model for Dense Nonaqueous Phase Liquid Source Identification in Groundwater

HouZeyu

DaiZhenxue

LaoWangmei

et al. 2019

Environmental Engineering Science

Self Cite

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“…In a large number of Monte Carlo experiments, the surrogate model can be invoked directly without the need to compute the simulation model extensively, which can greatly reduce the computational load and calculation time. (Hou and Lu, 2018).…”

Section: Support Vector Regression Surrogate Modelmentioning

confidence: 99%

Stochastic simulation of seawater intrusion in Longkou area, China

Fan¹,

Cui²,

et al. 2022

Preprint

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Seawater intrusion is a common groundwater pollution problem, which has a great impact on ecological environment and economic development. In this paper, a numerical simulation model of variable density groundwater was constructed to simulate and predict the future seawater intrusion in Longkou city, Shandong Province of China. The influence of the sensitive parameter uncertainty of the model on the simulation results was evaluated by using the Monte Carlo method. In order to reduce the computational load from repeatedly calling the simulation model, the surrogate model was established by using the Support Vector Regression (SVR) method. The research results indicate that the SVR surrogate model can fit the input-output relationship of the simulation model with high accuracy. The seawater intrusion in the Longkou area will gradually aggravate at a slow rate, and the area of seawater intrusion is estimated to be 67.45 to 77.07 km2 after 30 years with 80% confidence.

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“…The SVR model transforms complex low-dimensional non-linear regression problems into linear regression problems in high-dimensional feature space by applying a mapping function, Φ(x). The regression function is defined as follows [29]:…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Predicting the Water-Conducting Fracture Zone (WCFZ) Height Using an MPGA-SVR Approach

et al. 2020

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Mine water that inrushes from coal-roof strata has always posed a substantial threat to mining activities every year. Therefore, an accurate prediction of the water-conducting fracture zone (WCFZ) height in the mining overburden strata is of great significance for the prevention and control of mine water accidents. The support vector regression (SVR) is proposed to predict the height of the WCFZ based on the mining depth, hard rock proportional coefficient, mining thickness and length of the working face. Simultaneously, the multi-population genetic algorithm (MPGA) is employed to search for the optimal SVR parameters. The MPGA-SVR model is trained and tested with a total of 69 collected data samples, and it is also applied to a field test. The accuracy and stability of the model were measured by the mean squared error and correlation coefficients. The obtained results show that the MPGA-SVR model achieves a higher accuracy and stability than the traditional empirical formula and genetic algorithm (GA)-SVR model. In terms of the process for optimizing the SVR parameters, the MPGA can find the optimal parameters more quickly and accurately, and it can effectively overcome the problem of premature and slow convergence of the genetic algorithm (GA). The proposed model improves the prediction accuracy and stability, which will help to avoid accidents caused by the inrush of water inrush in mining overburden strata and protect the ecological environment of the mining area.

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Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites

Cited by 47 publications

References 38 publications

Application of Mixed-Integer Nonlinear Optimization Programming Based on Ensemble Surrogate Model for Dense Nonaqueous Phase Liquid Source Identification in Groundwater

Application of Mixed-Integer Nonlinear Optimization Programming Based on Ensemble Surrogate Model for Dense Nonaqueous Phase Liquid Source Identification in Groundwater

Stochastic simulation of seawater intrusion in Longkou area, China

Predicting the Water-Conducting Fracture Zone (WCFZ) Height Using an MPGA-SVR Approach

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