Seepage and dam deformation analyses with statistical models: support vector regression machine and random forest

Belmokre, Ahmed; Mihoubi, Mustapha Kamel; Santillán, David

doi:10.1016/j.prostr.2019.08.093

Cited by 21 publications

(8 citation statements)

References 8 publications

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“…This study suggested employing a new set of engineered slopes for confirming the obtained rules. Belmokre et al [48] analyzed the seepage through the dam by employing SVR and RF models. In fact, the seepage flow rate was estimated at various points of a roller-compacted concrete gravity dam.…”

Section: Other Soft Computing Methods For Seepage Modelingmentioning

confidence: 99%

“…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%

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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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Section: Other Soft Computing Methods For Seepage Modelingmentioning

confidence: 99%

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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“…Due to the influence of the natural environment and the lack of safety monitoring means and other factors, the working behavior of the dam will continue to change. It will bring some hidden dangers to the normal operation of the dam [2,3]. If these hidden dangers cannot be found in time and measures taken to solve them, catastrophic accidents such as dam catastrophe or even dam failure are likely to be caused.…”

Section: Introductionmentioning

confidence: 99%

IEALL: Dam Deformation Prediction Model Based on Combination Model Method

Jing

et al. 2023

Applied Sciences

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The accuracy of dam deformation prediction is a key issue that needs to be addressed due to the many factors that influence dam deformation. In this paper, a dam deformation prediction model based on IEALL (IGWO-EEMD-ARIMA-LSTM-LSTM) is proposed for a single-point scenario. The IEALL model is based on the idea of a combination model. Firstly, EEMD is used to decompose the dam deformation data, and then the ARIMA and LSTM models are selected for prediction. To address the problem of low prediction accuracy caused by simple linear addition of prediction results from different models in traditional combination models, the LSTM model is used to learn the combination relationship of different model prediction results. The problem of neural network parameters falling into local optima due to random initialization is addressed by using the improved gray wolf optimization (IGWO) to optimize multiple parameters in the IEALL combination model to obtain the optimal parameters. For the multi-point scenario of dam deformation, based on the IEALL model, a dam deformation prediction model based on spatio-temporal correlation and IEALL (STAGCN-IEALL) is proposed. This model introduces graph convolutional neural networks (GCN) to extract spatial features from multi-point sequences, increasing the model’s ability to express spatial dimensions. To address the dynamic correlation between different points in the deformation sequence at any time and the dynamic dependence on different points at any given time, spatio-temporal attention mechanisms are introduced to capture dynamic correlation from both spatial and temporal dimensions. Experimental results showed that compared to ST-GCN, IEALL reduced the RMSE, MAE, and MAPE by 16.06%, 14.72%, and 21.19%. Therefore, the proposed model effectively reduces the prediction error and can more accurately predict the trend of dam deformation changes.

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“…The ML models have the capability to fit nonlinear and high complex systems without explanation of the mechanism involved in these systems. In dam safety modeling, Artificial Neural Network (ANN) and support vector regression (SVR) are largely applied in modeling the safety of the concrete dams (Belmokre et al, 2019;Chen et al, 2021;de Granrut et al, 2019;Fisher et al, 2017;Kang et al, 2019;Mata, 2011).…”

Section: Introductionmentioning

confidence: 99%

Predicting Daily Pore Water Pressure in Embankment Dam Using Machine Learning Models and Hydrostatic Seasonal Time Approach

Bilali

Moukhliss

Taleb

et al. 2021

Preprint

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Prediction-based approaches are valuable in assessing dam safeties, as they allow comparing the actual measurements with the projected values to detect anomalies early. For two decades, machine learning (ML) algorithms have been developed and improved to help in accurately predicting the dam behaviors. However, the generalization ability (GA) of these models is not analyzed enough in dam engineering. In this study, the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Adaptive Boosting (AdaBoost) models with nonlinear autoregressive exogenous inputs (NARX) are evaluated and compared with the conventional Hydrostatic Seasonal Time (HST) model for predicting the daily pore water pressure in an embankment Dam. Moreover, we proposed a classification method of the model into four categories ‘’Perfect’’, ‘’Excellent’’, ‘’Good’’, and ‘’Poor’’ according to the GA. Results showed that, except for the AdaBoost, the other ML models outperformed the traditional statistical approach (HST) in terms of prediction accuracy as well as the GA. Overall; the study results provide new insights in enhancing the monitoring processes and dam safeties by detecting the anomalies early through the measurements and the selection of the best fitted-models.

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Seepage and dam deformation analyses with statistical models: support vector regression machine and random forest

Cited by 21 publications

References 8 publications

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

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

IEALL: Dam Deformation Prediction Model Based on Combination Model Method

Predicting Daily Pore Water Pressure in Embankment Dam Using Machine Learning Models and Hydrostatic Seasonal Time Approach

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