Review of machine learning-based surrogate models of groundwater contaminant modeling

Luo, Jiannan; Ma, Xi; Ji, Yefei; Li, Xueli; Song, Zhuo; Lu, Wenxi

doi:10.1016/j.envres.2023.117268

Cited by 9 publications

(3 citation statements)

References 161 publications

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“…Evaluating the prediction accuracy and efficiency of the surrogate model is essential, as inaccurate models can squander resources and impair optimization, predictions, and feasibility analysis [72]. In this study, the evaluation of surrogate model accuracy in approximating the simulation model involves the use of key metrics, namely the coefficient of determination (R 2 ), mean relative error (MRE), mean-squared error (MSE), and root meansquared error (RMSE).…”

Section: Variogram-based Global Sensitivity Analysis (Vars)mentioning

confidence: 99%

Surrogate-Based Uncertainty Analysis for Groundwater Contaminant Transport in a Chromium Residue Site Located in Southern China

Zou,

Yousaf,

Yang

et al. 2024

Water

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Numerical modeling is widely acknowledged as a highly precise method for understanding the dynamics of contaminant transport in groundwater. However, due to the intricate characteristics of environmental systems and the lack of accurate information, the results are susceptible to a significant degree of uncertainty. Numerical models must explicitly consider related uncertainties in parameters to facilitate robust decision-making. In a Chromium Residue Site located in southern China (the study area), this study employed Monte Carlo simulation to assess the impact of variability in key parameters uncertainty on the simulation outcomes. Variogram analysis of response surface (VARS), global sensitivity analysis, and an XGBoost (version 2.0.0)-based surrogate model was employed to overcome the substantial computational cost of Monte Carlo simulation. The results of numerical simulation indicate that the contaminant is spreading downstream towards the northern boundary of contaminated site near Lianshui River, threatening water quality. Furthermore, migration patterns are complex due to both downstream convection and upstream diffusion. Sensitivity analysis identified hydraulic conductivity, recharge rate, and porosity as the most influential model parameters, selected as key parameters. Moreover, uncertainty analysis indicated that the variability in key parameters has a minimal impact on the simulation outcomes at monitoring wells near the contaminant source. In contrast, at wells positioned a considerable distance from the contaminant source, the variability in key parameters significantly influences the simulation outcomes. The surrogate model markedly mitigated computational workload and calculation time, while demonstrating superior precision and effectively capture the non-linear correlations between input and output of the simulation model.

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Section: Variogram-based Global Sensitivity Analysis (Vars)mentioning

confidence: 99%

Surrogate-Based Uncertainty Analysis for Groundwater Contaminant Transport in a Chromium Residue Site Located in Southern China

Zou,

Yousaf,

Yang

et al. 2024

Water

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“…Existing research indicates that the accuracy of surrogate models varies in different application scenarios. The aforementioned surrogate model has achieved good results in various fields [29][30][31], but whether it can maintain the same accuracy when applied to the prediction of dock ship berthing collision forces still requires further verification. The "Input-Output" dataset is split into two parts: the training set is used to build different surrogate models, and the validation set is used to evaluate the training results.…”

Section: Evaluation and Optimization Of Surrogate Modelsmentioning

confidence: 99%

“…When constructing surrogate models, it is crucial to achieve an optimal balance between accuracy and computational cost. In this study, four surrogate models were constructed and trained using training sets of varying sizes N tra (i.e., 10,20,30,40), all under the same validation set N val = 20. The accuracy discrepancies among the different surrogate models were discussed in order to explore the best-case scenario that minimized computational effort while maximizing precision.…”

Section: Comparative Analysis Of Surrogate Modelsmentioning

confidence: 99%

High-Accuracy and Fast Calculation Framework for Berthing Collision Force of Docks Based on Surrogate Models

Zeng,

Zhu,

Wang

et al. 2024

JMSE

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The accurate prediction of the collision force magnitude resulting from ship berthing on docks is crucial for the rationality and safety of dock structural design. This paper presents a novel framework for the calculation of berthing collision force for ships (CBCF), which integrates field data, finite element models, and surrogate models. Based on field data and finite element analysis, the framework constructs and compares four surrogate models with low sample requirements, ultimately selecting the optimal surrogate model for predicting collision force. Furthermore, a sensitivity analysis of the parameters is conducted based on the selected model, followed by a comparison with the various methods used for collision force prediction. The results illustrate the effectiveness of the proposed framework in replacing finite element models for the rapid and accurate prediction of collision force. Comparison with existing methods also underscores the advantages of the proposed framework, including low sample requirements, high calculation accuracy, and exceptional efficiency. In summary, this study not only introduces a novel and precise surrogate model framework for the swift prediction of berthing collision force, but it also offers valuable insights into the prevention of ship collision with wharf accidents and facilitates the rational and safe design of wharf structures.

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Remote sensing, GIS, and analytic hierarchy process-based delineation and sustainable management of potential groundwater zones: a case study of Jhargram district, West Bengal, India

Guria,

Mishra,

Dutta

et al. 2023

Environ Monit Assess

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Review of machine learning-based surrogate models of groundwater contaminant modeling

Cited by 9 publications

References 161 publications

Surrogate-Based Uncertainty Analysis for Groundwater Contaminant Transport in a Chromium Residue Site Located in Southern China

Surrogate-Based Uncertainty Analysis for Groundwater Contaminant Transport in a Chromium Residue Site Located in Southern China

High-Accuracy and Fast Calculation Framework for Berthing Collision Force of Docks Based on Surrogate Models

Remote sensing, GIS, and analytic hierarchy process-based delineation and sustainable management of potential groundwater zones: a case study of Jhargram district, West Bengal, India

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