Artificial neural network modeling of cross-shore profile on sand beaches: The coast of the province of Valencia (Spain)

López, Isabel; Aragonés, Luís; Villacampa, Yolanda; Compañ, Patricia

doi:10.1080/1064119x.2017.1385666

Cited by 15 publications

(10 citation statements)

References 45 publications

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“…The optimal node numbers in the hidden layer obtained from literature and the calculated ranges from proposed guidelines are given in Table 6. It shows that the optimal values in five publications are in the range defined by Equations (11)- (13), except that the optimal node number in literature [24] is slightly different to the calculated range. Four [19,20,22,23] of them are in the range with an assumption PF equal to 10 and one [15] in the range with PF equal to 70.…”

Section: Lower Boundary Upper Boundarymentioning

confidence: 82%

“…One empirical equation may show consistent performance for the problems within similar complexity. For instance, the prediction of Equation (3) for beach erosion [13] is rather close to the optimal value that was used as the rule of thumb in the study of the mechanical behavior of mortar [22]. The second reason is the quality of the dataset for training.…”

Section: Introductionmentioning

confidence: 92%

“…= ( ) = 0.2982 − 0.001242 (1 − 0.08836 * ) (13) 0. * The range includes the optimal node number in the literature.…”

Section: Lower Boundary Upper Boundarymentioning

confidence: 99%

“…Table 1 lists the publications on the optimization of the single hidden layer node number. To seek an optimal one, many applied the trial and error method in a diverse range of the node numbers [13][14][15][16][17]; whilst others merely followed the rules of thumb proposed in the literature [18][19][20][21][22][23][24], and the application of rules can significantly reduce the number of trials. Six selected rules of thumb [18][19][20][21][22][23][24][25] used in the respective publication are given below and indicated in Table 1 with an asterisk.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining

et al. 2019

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Artificial neural network (ANN) is widely applied as a predictive tool to solve complex problems. The performance of an ANN model is significantly affected by the applied architectural parameters such as the node number in a hidden layer, which is largely determined by the complexity of cases, the quality of the dataset, and the sufficiency of variables. In the present study, the impact of variation/response space complexity and variable completeness on backpropagation (BP) ANN model establishment was investigated, with a steel ladle lining from secondary steel metallurgy as the case study. The variation dataset for analysis comprised 160 lining configurations of ten variables. Thermal and thermomechanical responses were obtained via finite element (FE) modeling with elastic material behavior. Guidelines were proposed to define node numbers in the hidden layer for each response as a function of the node number in the input layer weighted with the percent value of the significant variables contributing above 90% to the response, as well as the node number in the output layer. The minimum numbers of input variables required to achieve acceptable prediction performance were three, five, and six for the maximum compressive stress, the end temperature, and the maximum tensile stress. empirical equations into other fields are far less satisfying. Equations (1) and (2) show more robust applications, yet they yield a rather larger number for trials.

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Section: Lower Boundary Upper Boundarymentioning

confidence: 82%

Section: Introductionmentioning

confidence: 92%

“…= ( ) = 0.2982 − 0.001242 (1 − 0.08836 * ) (13) 0. * The range includes the optimal node number in the literature.…”

Section: Lower Boundary Upper Boundarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining

et al. 2019

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“…Besides they have been shown to have some inconsistencies between measured and modeled data [3]. On the contrary, artificial neural networks (ANNs) have been introduced in the field, and we have seen a dramatic increase in accuracy at much lesser costs [4][5][6][7]. Neural networks mimic how the brain works and are often dependent on activation/transfer functions and widely used in many fields, such as Yang et al mentioned that many electric utilities use machine learning-based outage prediction models (OPM) to predict the impact of storms on their networks for sustainable management [8].…”

Section: Introductionmentioning

confidence: 99%

Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network

Chen

Wang

2020

Water

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The study has modeled shoreline changes by using a multilayer perceptron (MLP) neural network with the data collected from five beaches in southern Taiwan. The data included aerial survey maps of the Forestry Bureau for years 1982, 2002, and 2006, which served as predictors, while the unmanned aerial vehicle (UAV) surveyed data of 2019 served as the respondent. The MLP was configured using five different activation functions with the aim of evaluating their significance. These functions were Identity, Tahn, Logistic, Exponential, and Sine Functions. The results have shown that the performance of an MLP model may be affected by the choice of an activation function. Logistic and the Tahn activation functions outperformed the other models, with Logistic performing best in three beaches and Tahn having the rest. These findings suggest that the application of machine learning to shoreline changes should be accompanied by an extensive evaluation of the different activation functions.

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Coastal forecast through coupling of Deep Learning and hydro-morphodynamical modelling

Kumar

Leonardi

2022

Preprint

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As climate-driven risks for the world's coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change and informed coastal management choices. Artificial Intelligence, especially deep learning, is a powerful technology that has been rapidly evolving over the last couple of decades and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of deep learning models were tested, one set relying on localized modelling outputs or localized data sources and one set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing. The second set of reduced-dependency models provides regression values greater than 0.84 and 0.76 for training and testing. Both model types require a running time of the order of minutes, compared to the several hours of running times of the hydrodynamic models.Our results highlight the potential of deep learning and statistical models for coastal applications.

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Artificial neural network modeling of cross-shore profile on sand beaches: The coast of the province of Valencia (Spain)

Cited by 15 publications

References 45 publications

Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining

Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining

Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network

Coastal forecast through coupling of Deep Learning and hydro-morphodynamical modelling

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