Below the Data Range Prediction of Soft Computing Wave Reflection of Semicircular Breakwater

Kundapura, Suman; Hegde, Arkal Vittal; Pinho, José L. S.

doi:10.1007/s11804-019-00088-4

Cited by 8 publications

(1 citation statement)

References 25 publications

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“…In each of the other nodes (nonleaf nodes), a decision is made according to one or more specifc attributes. Te decision tree is a popular data mining technique because of its simplicity and comprehensibility; in other words, the decision tree alone describes everything and does not need an expert to interpret the output [35]. In fact, it is a graphical method, and because of its interpretation, it may be easier to classify than other techniques.…”

Section: M5 Model Treementioning

confidence: 99%

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Chen

Rashid

Sayed

et al. 2023

Advances in Civil Engineering

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In coastal and port engineering, wind-generated waves have always been a crucial, fundamental, and important topic. As a result, various methods for estimating wave parameters, including field measurement and numerical methods, have been proposed over time. This study evaluates the wave height at Sri-Lanka Hambantota Port using soft computing models such as Artificial Neural Networks (ANNs) and the M5 model tree (M5MT). In order to overcome its nonstationarity, the primary wave height time series were divided into subtime series using the wavelet transform. The collected subtime series were then utilized as input data for ANN and M5MT in order to determine the wave height. For the sake of the model performance, the daily wind and wave data from the Acoustic Wave and Current (AWAC) sensor for Hambantota Port in 2020 and Sanmen Bay in 2017 were used in this study. The training state utilizes 80% of the available data, while the test state uses 20%. The Root Mean Square Error (RMSE) of the ANN, M5, WANN, and Wavelet-M5 models in the Hambantota Port for the test stage are 0.12, 0.11, 0.04, and 0.06, respectively. While in Sanmen Bay, the RMSE of the ANN, M5, WANN, and Wavelet-M5 models for the test stage are 0.14, 0.16, 0.06, and 0.08, respectively. According to the findings of this study, the accuracy of WANN and Wavelet-M5 hybrid models in evaluating wave height is superior to that of classic ANN and M5MT, and it is recommended that WANN and Wavelet-M5 hybrid models be used to estimate wave height.

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Section: M5 Model Treementioning

confidence: 99%

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Chen

Rashid

Sayed

et al. 2023

Advances in Civil Engineering

View full text Add to dashboard Cite

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Numerical and experimental investigation on the hydrodynamic characteristics of an arc-shaped plate-type breakwater under the action of long-period waves

Wang

et al. 2021

Ocean Engineering

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A comparative study of the hydrodynamic characteristics of permeable twin-flat-plate and twin-arc-plate breakwaters based on physical modeling

Wang

et al. 2021

Ocean Engineering

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Below the Data Range Prediction of Soft Computing Wave Reflection of Semicircular Breakwater

Cited by 8 publications

References 25 publications

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Numerical and experimental investigation on the hydrodynamic characteristics of an arc-shaped plate-type breakwater under the action of long-period waves

A comparative study of the hydrodynamic characteristics of permeable twin-flat-plate and twin-arc-plate breakwaters based on physical modeling

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