Predicting daily pore water pressure in embankment dam: Empowering Machine Learning-based modeling

Bilali, Ali El; Moukhliss, Mohammed; Taleb, Abdeslam; Nafii, Ayoub; Alabjah, Bahija; Brouziyne, Youssef; Mazigh, Nouhaila; Teznine, Khalid; Mhamed, Madark

doi:10.1007/s11356-022-18559-7

Cited by 22 publications

(10 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even if a large number of features are missing, RF can still maintain accuracy. This method has been well applied in the prediction of PWP [34,35].…”

Section: Methodsmentioning

confidence: 99%

“…Wei et al [3] showcased the enhanced precision and robustness of gated recurrent unit (GRU) and Long Short-Term Memory (LSTM) models in predicting slope seepage/stability under rainfall compared to the standard RNNs. RF has been applied to the prediction of daily pore water pressure in the embankment [34,35], and MLP has been used in the case of concrete dam seepage behavior [9]. RNNs and GRU are applied in pore water pressure prediction of a tunnel boring machine [36] and slope [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pore Water Pressure Prediction Based on Machine Learning Methods—Application to an Earth Dam Case

An,

Dias,

Carvajal

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Pore water pressure (PWP) response is significant for evaluating the earth dams’ stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order to detect a pathology or anomaly in the behavior of an embankment dam. This study endeavors to tackle this challenge through the application of diverse machine learning (ML) techniques in estimating the PWP within an existing earth dam. The methods employed include random forest (RF) combined with simulated annealing (SA), multilayer perceptron (MLP), standard recurrent neural networks (RNNs), and gated recurrent unit (GRU). The prediction capability of these techniques was gauged using metrics such as the coefficient of determination (R2), mean square error (MSE), and CPU training time. It was found that all the considered ML methods could give satisfactory results for the PWP estimation. Upon comparing these methods within the case study, the findings suggest that, in this study, multilayer perceptron (MLP) gives the most accurate PWP prediction, achieving the highest coefficient of determination (R2 = 0.99) and the lowest mean square error (MSE = 0.0087) metrics. A sensitivity analysis is then presented to evaluate the models’ robustness and the hyperparameter’s influence on the performance of the prediction model.

show abstract

“…Even if a large number of features are missing, RF can still maintain accuracy. This method has been well applied in the prediction of PWP [34,35].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pore Water Pressure Prediction Based on Machine Learning Methods—Application to an Earth Dam Case

An,

Dias,

Carvajal

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…However, to rank the models for simulation purposes, EL Bilali et al [47] suggested the classification of the machine learning models according to categories of perfect, excellent, good, and poor models in terms of GA as follows:…”

Section: Rmsementioning

confidence: 99%

Predicting Daily Suspended Sediment Load Using Machine Learning and NARX Hydro-Climatic Inputs in Semi-Arid Environment

Ezzaouini

Mahé

Kacimi

et al. 2022

Water

Self Cite

View full text Add to dashboard Cite

Sediment transport in basins disturbs the ecological systems of the water bodies and leads to reservoir siltation. Its evaluation is crucial for managing water resources. The practical application of the process-based model can confront some limitations noticed in the lower accuracy during the validation process due to the lack of reliable physical datasets. In this study, we attempt to apply machine-learning-based modeling (ML) to predict the suspended sediment load, using hydro-climatic data as input variables in the semi-arid Bouregreg basin, Morocco. To that end, data for the years 2016 to 2020 were used for the training process, and the validation was performed with 2021 data. The results showed that most ML models have good accuracy, with a Nash–Schiff efficiency (NSE) ranging from 0.47 to 0.80 during the validation phase, which indicates satisfactory performances in predicting the SSL. Furthermore, the models were ranked against their generalization ability (GA), which revealed that the developed models are good to excellent in terms of GA. Overall, the present study provides new insight into predicting the SSL in a semi-arid environment, such as the Bouregreg basin.

show abstract

“…With the advancement of computer technology, machine learning techniques have gradually been applied to the analysis of dam safety monitoring data [33,34]. Intelligent monitoring models such as random forests, neural networks, extreme learning machines, and others have shown significant improvements in prediction accuracy compared to traditional models, enabling more accurate predictions of dam safety states [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

A Dam Safety State Prediction and Analysis Method Based on EMD-SSA-LSTM

Yang,

Xiang,

Wang

et al. 2024

Water

View full text Add to dashboard Cite

The safety monitoring information of the dam is an indicator reflecting the operational status of the dam. It is a crucial source for analyzing and assessing the safety state of reservoir dams, possessing strong real-time capabilities to detect anomalies in the dam at the earliest possible time. When using neural networks for predicting and warning dam safety monitoring data, there are issues such as redundant model parameters, difficulty in tuning, and long computation times. This study addresses real-time dam safety warning issues by first employing the Empirical Mode Decomposition (EMD) method to decompose the effective time-dependent factors and construct a dam in a service state analysis model; it also establishes a multi-dimensional time series analysis equation for dam seepage monitoring. Simultaneously, by combining the Sparrow Optimization Algorithm to optimize the LSTM neural network computation process, it reduces the complexity of model parameter selection. The method is compared to other approaches such as RNN, GRU, BP neural networks, and multivariate linear regression, demonstrating high practicality. It can serve as a valuable reference for reservoir dam state prediction and engineering operation management.

show abstract

Predicting daily pore water pressure in embankment dam: Empowering Machine Learning-based modeling

Cited by 22 publications

References 56 publications

Pore Water Pressure Prediction Based on Machine Learning Methods—Application to an Earth Dam Case

Pore Water Pressure Prediction Based on Machine Learning Methods—Application to an Earth Dam Case

Predicting Daily Suspended Sediment Load Using Machine Learning and NARX Hydro-Climatic Inputs in Semi-Arid Environment

A Dam Safety State Prediction and Analysis Method Based on EMD-SSA-LSTM

Contact Info

Product

Resources

About