Machine learning and transport simulations for groundwater anomaly detection

Liu, Jiangguo; Gu, Jianli; Li, Huishu; Carlson, Kenneth

doi:10.1016/j.cam.2020.112982

Cited by 30 publications

(12 citation statements)

References 21 publications

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“…Model‐simulated or synthetic data sets are commonly used to evaluate parameter estimation and predictive modeling routines in hydrology (Mirus et al 2011; Liu et al. 2020). For this study, the groundwater model used to generate synthetic data was initially designed to evaluate the efficacy of pump‐and‐treat scenarios for the containment of site contaminants (DERS 1997).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Groundwater Contaminant Plume Development Using Statistical and Machine Learning Methods

McConnell

Askarani

Cognac

et al. 2022

Groundwater Monitoring Rem

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This column reviews the general features of PHT3D Version 2, a reactive multicomponent transport model that couples the geochemical modeling software PHREEQC-2 (Parkhurst and Appelo 1999) with three-dimensional groundwater flow and transport simulators MODFLOW-2000 and MT3DMS (Zheng and Wang 1999). The original version of PHT3D was developed by Henning Prommer and Version 2 by Henning Prommer and Vincent Post (Prommer and Post 2010). More detailed information about PHT3D is available at the website http://www.pht3d.org.The review was conducted separately by two reviewers. This column is presented in two parts.

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Section: Methodsmentioning

confidence: 99%

“…Advances in data‐driven, empirical modeling have been made and these tools are increasingly adopted for use in water resources and geology (Razavi et al 2012; Yadav et al 2018; Dramsch 2020; Liu et al. 2020). These studies have demonstrated the applicability of machine learning methods to environmental data.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Groundwater Contaminant Plume Development Using Statistical and Machine Learning Methods

McConnell

Askarani

Cognac

et al. 2022

Groundwater Monitoring Rem

View full text Add to dashboard Cite

show abstract

“…The findings of the proposed study suggest that data-driven modeling approaches are highly effective in predictive modeling and decision making; however, there is a need to improve the accuracy of these systems. Article [33] Proposed SVM-based model to detect anomalies in groundwater using real-time data. Another study [34] presented a machine learning-based solution for predicting lithology formation based on drilling parameters.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Optimization-Based Voting Classifier and Chained Multi-Objective Regressor for Effective Groundwater Resource Management

Rizwan¹,

Khan

Iqbal

et al. 2021

IEEE Access

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Water is an essential source of life for every living thing, and drilling is the only source to gain water from underground. Different advanced technologies have been used to minimize the time factor and labor force. Along with technology to be used, some other factors are equally essential to be considered, like water level, the hardness level of the land, and the number of days spent on the whole process. The study proposed a weighted voting classifier based on Differential Evaluation (DE) to classify the regions with different soil colors and land layers. The weights are assigned to the candidate classifiers based on their performance for each class. For the assignment of the optimal weight, the DE optimization algorithm is used. Moreover, the study presents a chained multi-objective regression model to simultaneously predict the water level and total depth on different locations. The proposed work facilitates the drilling industry to increase the rate of penetration (ROP) by selecting the region with soft soil and land layer. The prediction of depth and water level allows the industry to estimate water levels in different areas at different depths. The dataset is provided by the research organization, which contains information of different drilling points. The results of the proposed weighted voting classifier are compared with the traditional machine learning models (kernel Naive Bayes, Gaussian SVM, Quadratic SVM, and Bilayered Neural network) and state of the art voting classifier in terms of precision, recall, and accuracy. Moreover, the proposed regression model is evaluated by well-known evaluation metrics, including Mean Absolute Error, Mean Square Error, and R2 score. Finally, the comparison verifies the effectiveness of the enhanced optimization-based classifier and multi-objective regressor.

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“…As mentioned in the previous section, Sharghi et al [52] simulated the seepage of an earthfill dam in Iran by employing SVR and FFNN, ANFIS, and ARIMA models. Furthermore, Belmokre et al [48] analyzed the seepage through dam by employing SVR; in the field of groundwater modeling, Liu et al [57] developed a framework by employing SVR to identify the groundwater anomaly. In this way, conductivity and four surrogates were employed to detect the groundwater anomaly.…”

Section: Support Vector Machine (Svm) For Seepage Modelingmentioning

confidence: 99%

The Applications of Soft Computing Methods for Seepage Modeling: A Review

Nourani

Behfar

Dąbrowska

et al. 2021

Water

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In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sources, to ensure adequate management of scarce water resources. On the other hand, excessive seepage can threaten the stability of earthfill dams and infrastructures. Furthermore, it could result in severe soil erosion and consequently cause environmental damage. Considering the complex and nonlinear nature of the seepage process, employing soft computing techniques, especially applying pre-post processing techniques as hybrid methods, such as wavelet analysis, could be appropriate to enhance modeling efficiency. This review paper summarizes standard soft computing techniques and reviews their seepage modeling and simulation applications in the last two decades. Accordingly, 48 research papers from 2002 to 2021 were reviewed. According to the reviewed papers, it could be understood that regardless of some limitations, soft computing techniques could simulate the seepage successfully either through groundwater or earthfill dam and hydraulic structures. Moreover, some suggestions for future research are presented. This review was conducted employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) method.

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Machine learning and transport simulations for groundwater anomaly detection

Cited by 30 publications

References 21 publications

Forecasting Groundwater Contaminant Plume Development Using Statistical and Machine Learning Methods

Forecasting Groundwater Contaminant Plume Development Using Statistical and Machine Learning Methods

Enhanced Optimization-Based Voting Classifier and Chained Multi-Objective Regressor for Effective Groundwater Resource Management

The Applications of Soft Computing Methods for Seepage Modeling: A Review

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