Machine learning techniques for estimating wave-overtopping discharges at coastal structures

Elbisy, Moussa S.

doi:10.1016/j.oceaneng.2023.113972

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…Every new base model aims to fix the errors created by the previous base models. The boosting strategy was initially devised in response to Kearns' question (8) as follows ( 9) is one strong learner the same as a group of weak learners? A weak leaner is an algorithm that just marginally outperforms random guessing; a strong base model is a more accurate prediction or classification method that outperforms random guessing.…”

Section: Methodsmentioning

confidence: 99%

“…Elbisy (8) conducted a study on the estimation of wave-overtopping discharge at coastal structures with a straight slope using different machine learning algorithms. Specifically, they used multilayer perceptron (MPNN), cascade correlation neural network (CCNN), and GRNN, as well as support vector machines (SVMs) for this purpose.…”

Section: Introductionmentioning

confidence: 99%

“…The study's findings could have practical applications in the field of coastal engineering, such as in designing and optimizing coastal structures to withstand wave-overtopping. Alshahri and Elbisy (8) employed artificial neural networkbased (ANN) approaches with different algorithms, such as MPNN, and GRNN, and SVM for estimating the wave overtopping discharge at rubble mound structures featuring a straight slope. They found that the GRNN produced exceptionally precise results.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Between Support Vector Machine and Gradient Boosted Trees Models for Prediction of Wave Overtopping at Coastal Structures with Composite Slope

Alqahtani,

Elbisy,

Osra

2023

IJST

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Background/Objectives: Predicting wave overtopping at coastal structures is a critical task in coastal engineering. The use of machine learning models can help predict wave overtopping with higher accuracy and efficiency. Methods: In this study, the accuracy of support vector machine (SVM) and gradientboosted tree (GBT) approaches for predicting wave overtopping discharge of coastal structures with composite slopes "without a berm" was evaluated and compared. The newly developed EurOtop database was used for this study. Findings: The results showed that the GBT technique provided more accurate predictions than the SVM technique. Notably, the results demonstrated that the GBT model significantly decreased the overall error and accurately estimated the wave-overtopping discharge. Compared to the SVM model, the scatter index calculated using the GBT (0.57) is lower than that of the SVM (o.94). In terms of R-index, the GBT (0.97) was superior to the SVM (0.92) model. Novelty: This study provides an effective approach for predicting wave overtopping discharge in the design and management of coastal structures.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparing Between Support Vector Machine and Gradient Boosted Trees Models for Prediction of Wave Overtopping at Coastal Structures with Composite Slope

Alqahtani,

Elbisy,

Osra

2023

IJST

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“…In recent years, with advancements in data science and computational resources, Artificial Intelligence (AI) in the form of Machine Learning (ML) has been successfully employed to address a wide range of coastal engineering problems. For example, significant research relating to the development of AI based decision-support algorithms for the prediction of wave characteristics (Yeganeh-Bakhtiary et al, 2023) and wave overtopping at coastal defences has been undertaken (see, for example, den Bieman et al, 2021aBieman et al, , 2021bden Bieman et al, 2020;Elbisy, 2023;Elbisy and Elbisy, 2021;Habib et al, 2022b;Habib et al, 2023a;Habib et al, 2023b). Habib et al (2022a) has provided an overview of recent studies on the applications of ML approaches in coastal engineering problems.…”

Section: S Tmax H Smentioning

confidence: 99%

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Habib,

Abolfathi,

O’Sullivan

et al. 2024

Front. Built Environ.

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Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making.

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“…The evaluation of the overtopping predictions using a prototype dataset demonstrated that the proposed CNN model exceeded the performance of previous ML models. In [13], a variety of ML methods, such as MPNN, Cascade Correlation Neural Networks (CCNN), GRNN, and SVMs, were deployed to predict wave-overtopping discharge in coastal structures with a linear slope. The results indicated that the GRNN had a notable degree of precision.…”

Section: Introductionmentioning

confidence: 99%

Estimation οf Wave Overtopping Discharges at Coastal Structures with Combined Slopes using Machine Learning Techniques

Elbisy

2024

Eng. Technol. Appl. Sci. Res.

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Coastal defense structures are of paramount importance in protecting coastal communities from the adverse impacts of severe weather events and flooding. This study uses machine learning techniques, specifically Decision Tree (DT), Gradient Boosted Tree (GBT), and Support Vector Machine (SVM) models, to estimate wave overtopping discharge at coastal structures with combined slopes employing the recently built EurOtop database. The models were evaluated by deploying statistical metrics and Taylor diagram visualization. The GBT model demonstrated a high level of accuracy in predicting wave-overtopping discharge. Compared to the other models, the scatter index of GBT (0.392) was lower than that of DT (0.512) and SVM (0.823). In terms of the R-index, GBT (0.991) was superior to DT (0.977) and SVM (0.943). The GBT results were also compared with those of previous works. The findings showed that the GBT model significantly decreased the overall error and provided accurate estimations of the wave-overtopping discharge.

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Machine learning techniques for estimating wave-overtopping discharges at coastal structures

Cited by 5 publications

References 19 publications

Comparing Between Support Vector Machine and Gradient Boosted Trees Models for Prediction of Wave Overtopping at Coastal Structures with Composite Slope

Comparing Between Support Vector Machine and Gradient Boosted Trees Models for Prediction of Wave Overtopping at Coastal Structures with Composite Slope

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Estimation οf Wave Overtopping Discharges at Coastal Structures with Combined Slopes using Machine Learning Techniques

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