Research on Prediction of Dam Seepage and Dual Analysis of Lag-Sensitivity of Influencing Factors Based on MIC Optimizing Random Forest Algorithm

Liu, Yongtao; Zheng, Dongjian; Wu, Xin; Chen, Xingqiao; Georgakis, Christos Τ.; Qiu, Jianchun

doi:10.1007/s12205-022-0611-6

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Application instances indicate high fitting accuracy of the model, with an R 2 reaching 0.98. Liu Yongtao [25] employed the HTRT (hydrostatic-thermal-rainfall-time) model to identify the time-series influencing factors of seepage flow. The HTRT model nested the Random Forest algorithm to establish a seepage flow prediction model for dams.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dike seepage pressure based on ISSA-BiLSTM

Chen,

Liu,

Xing

et al. 2024

Preprint

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Seepage behavior is one of the critical factor in the operational safety of dams, and predicting dam seepage is the key content in dam monitoring and safety assessment research. The existing traditional dam seepage pressure prediction models have problems such as easy to fall into local optimum and limited predictive efficiency. The sparrow search algorithm(SSA) was improved as ISSA using both methods nonlinear Sine Cosine optimization algorithm and adaptive producer and scrounger ratio. We combined the Bidirectional Long Short-Term Memory (BiLSTM) neural network model with ISSA to develop the ISSA-BiLSTM seepage pressure prediction model. And the critical feature factors were extracted based on LightGBM to construct the input layer for seepage pressure prediction. The results show that the ISSA-BiLSTM model's fitting outcomes are generally consistent with the observed changes in seepage pressure observations, achieving an R2 of 0.987. In comparison to SSA-BiLSTM and BiLSTM, the model exhibits a substantial reduction in errors, decreasing by approximately 20% and 30%, respectively. This model can provide technical support and insights for accurately predicting dam seepage, contributing to the advancement of this field.

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Section: Introductionmentioning

confidence: 99%

Prediction of dike seepage pressure based on ISSA-BiLSTM

Chen,

Liu,

Xing

et al. 2024

Preprint

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“…The design, construction, maintenance, and rehabilitation of hydraulic structures present significant challenges due to the impact of seepage on their stability [1,2]. Ongoing global research in this specialized field is evident in the studies conducted by [3][4][5][6][7][8][9][10][11][12][13]. The construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile River has caused changes in the downstream regime and sediment load [14], leading to degradation along the Nile River.…”

Section: Introductionmentioning

confidence: 99%

“…Equations to calculate the distribution of the upwards pressure, gradient at the exit point, and seepage flow are derived, and a computer code is developed to determine the seepage parameters. According to the best knowledge of the authors, such issues related to the two structures founded on a finite stratum have not been addressed in the past [10,11,22,26]. Therefore, these tasks constitute original and novel research work that will provide valuable results for the engineering community.…”

Section: Introductionmentioning

confidence: 99%

Influence of a Subsidiary Weir on the Stability of a Main Structure Built on a Finite Stratum

Ghazaw,

Ghumman,

Al-Janabi

et al. 2023

Fluids

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Some dams globally have negatively affected downstream structures. Constructing subsidiary weirs may solve this problem. This novel study focuses on investigating the parameters of seepage beneath the original structure and the proposed subsidiary weir. Conformal mapping and finite element methods are used for the analysis. The proposed subsidiary weir consists of a sloping central apron, flat aprons on both the downstream and upstream ends, and upstream and downstream sheet piles of varying depths. The existing structure also has sheet piles of different depths at its upstream and downstream ends, with an impervious layer situated at a specific depth below both the structures. The study derives equations for the simulation of the upwards pressure on both the structures, seepage rate, and exit gradient along the downstream bed and the filter at an intermediate location. Our own developed software for the analysis and a commercial software for numerical methods named Finite Element Heat Transfer (FEHT)-version-1are used to calculate these parameters. The accuracy of the analytical and numerical methods is verified by comparing the results with experimental data, which demonstrate a good level of agreement. This study also simulates the impacts of various factors, such as sheet pile configurations, the depth of the stratum beneath the structure, the ratio of effective heads, and the length of the intermediate filter.

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Monitoring model group of seepage behavior of earth-rock dam based on the mutual information and support vector machine algorithms

Jiang

2024

Structural Health Monitoring

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The establishment of a high-precision piezometric water level monitoring model ensures the safe operation of earth-rock dams. The hysteresis effect of the upstream water level and rainfall should be considered during modeling. In the traditional method, the average factors are used to express this effect, and linear regression modeling is adopted. These factors reduce the accuracy of the model. In this paper, the mutual information (MI) and support vector machine (SVM) algorithms are proposed. MI has a powerful correlation analysis capability, and it is innovatively used to address hysteresis effects. SVM has a strong nonlinear modeling ability, and it is used as a modeling algorithm. During this study, it was found that the lag time of rainfall varied. In view of this characteristic, the concept of an innovative model group, which is an important extension of the traditional single model, is proposed. In the example, the mean square error (MSE) is used as the precision index. Compared with the traditional single model established by linear regression, the MSE of the MI–SVM model group can be reduced by approximately 60.98%–68.75%. Compared with the model group established by linear regression, the MSE of the MI–SVM model group can be reduced by approximately 41.28%–45.45%. The new method effectively improves the accuracy of the model and can precisely monitor the seepage state of the dam. Moreover, it is beneficial for improving the level of dam safety management and can be extended to other fields involving hysteresis effects and nonlinear modeling.

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Research on Prediction of Dam Seepage and Dual Analysis of Lag-Sensitivity of Influencing Factors Based on MIC Optimizing Random Forest Algorithm

Cited by 5 publications

References 43 publications

Prediction of dike seepage pressure based on ISSA-BiLSTM

Prediction of dike seepage pressure based on ISSA-BiLSTM

Influence of a Subsidiary Weir on the Stability of a Main Structure Built on a Finite Stratum

Monitoring model group of seepage behavior of earth-rock dam based on the mutual information and support vector machine algorithms

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