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In the present study, various ocean wave parameters are estimated from theoretical Pierson-Moskowitz spectra as well as measured ocean wave spectra using backpropagation neural networks (BNN). Ocean wave parameters estimation by BNN shows that the correlations are very close to one. This substantiates the use of neural networks (NN). For Indian coast, Scott spectra are used as it reasonably represents the measured spectra. The correlations of NN and Scott spectra are also compared. Once the network is trained, the ocean wave parameters can be estimated for unknown measured spectra, whereas significant wave height and spectral peak period are required to first generate the Scott spectra and then estimate other ocean wave parameters. © 2005 Elsevier Ltd. All rights reserved.
(Ocean wave parameters estimation using backpropagation neural networks) Mandal, S.; Rao, S.; Raju, D.H.
2005
Cyclone generated waves play a significant role in the design of coastal and offshore structures. Instead of conventional numerical models, neural network approach is used in the present study to estimate the wave parameters from cyclone generated wind fields. Eleven cyclones, which crossed the southern east coast of India between 1962 and 1979, are considered for analysis in this paper. The parametric hurricane wave prediction model by Young (1988) [Young, I.R., 1988. Parametric hurricane wave prediction model. Journal of Waterways Port Coastal and Ocean Engineering 114(5), 637-652] is used for hindcasting the wave heights and periods. Estimation of wave heights and periods is carried out using back propagation neural network with three updated algorithms, namely Rprop, Quickprop and superSAB. In neural network, the estimation is carried out using (i) difference between central and peripheral pressure, radius of maximum wind and speed of forward motion of cyclone as input nodes and the wave heights and periods as output nodes and (ii) wind speed and fetch as input nodes and wave heights and periods as output nodes. The estimated values using neural networks match well with those estimated using Young's model and a high correlation is obtained namely (0.99). © 2005 Elsevier Ltd. All rights reserved.
(Hindcasting of storm waves using neural networks) Rao, S.; Mandal, S.
2005
Prediction of wave reflection for quarter circle breakwaters using soft computing techniques
(National Institute of Science Communication and Policy Research, 2022) Ramesh, N.; Bhaskaran, S.; Rao, S.
The modified form of the semi-circular breakwater is called Quarter-Circle Breakwater (QBW). It consists of a quarter-circular surface facing incident waves, a horizontal bottom, a rear wall, and is built on a rubble mound foundation. In general, QCB may be constructed as emerged, with and without perforations that may be on one side or either side based on the coastal designer. These perforations dissipate the energy due to the formation of eddies and turbulence created inside the hollow chamber. In the present study, experimental data obtained from Binumol, 2017 are fed as input to both the models. This data is used to predict the reflection coefficient of QBW by adopting the ANN system approach. The reliability of the Artificial Neural Network (ANN) approach is done with statistical parameters, namely Model Performance Analysis (MPA) viz., Correlation Coefficient (CC), Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI). The results of the MPA indicate that the ANN is suited for predicting the reflection coefficient of QBW. © 2022 National Institute of Science Communication and Information Resources (NISCAIR). All rights reserved.
Predicting wave reflection coefficient of vertical caisson breakwater using machine learning: A data-driven approach
(Elsevier Ltd, 2025) Shankara Krishna, A.; Rao, M.; Rao, S.
Coastal zones are vital for ecological balance and human development, but are increasingly threatened by wave activity, shoreline erosion, and sea-level rise. To mitigate these challenges, engineers employ coastal protection structures. Specifically, vertical caisson breakwaters are preferred in deeper waters due to their advantages. Reflection Coefficient is an important hydrodynamic performance indicator for breakwaters. Recently, machine learning (ML) has been used for predicting coastal engineering parameters, offering an efficient means to support or augment traditional physical model studies, particularly during preliminary design phases, if sufficient quality data is available. This research focuses on using ML models to estimate the reflection coefficient of vertical caisson breakwaters based on a limited set of experimental data. Four different algorithms- Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB)- are developed and evaluated. Hyperparameters are optimised using a hybrid approach, combining Grid Search with manual refinement. Of the four models, XGB achieved the highest prediction accuracy (Test CC = 0.9631), while Random Forest exhibited the smallest generalisation gap, indicating strong consistency across datasets. The findings from the study suggest that XGB offers an efficient tool for early-stage design applications in coastal engineering. © 2025 Elsevier Ltd

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