Prediction model for occurrence of impact wave force

Mase, Hajime; Kitano, Toshikazu

doi:10.1016/s0029-8018(98)00037-7

Cited by 31 publications

(6 citation statements)

References 5 publications

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“…Tsai and Lee (1999) utilised neural networks for forecasting tidal variability used neural networks for forecasting wave heights at near-shore locations, using measurements from other locations as input, finding that using multiple sites as input increased the accuracy of predictions. Some work has tested ANNs for specific oceanic structural and engineering tasks: (Mase and Kitano, 1999) used feed-forward networks to estimate wave force impact on a marine structure, and (Mase et al, 1995) found that a similar ANN architecture accurately predicted damage levels on a breakwater resulting from wave action. ANNs have recently shown to produce superior estimates of wave spectra from wave parameters than those provided by theoretical or statistical predictions (Naithani and Deo, in print, 2005).…”

Section: Introductionmentioning

confidence: 99%

Near-shore swell estimation from a global wind-wave model: Spectral process, linear, and artificial neural network models

Browne

Castelle

Strauss

et al. 2007

Coastal Engineering

103

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Estimation of swell conditions in coastal regions is important for a variety of public, government, and research applications. Driving a model of the near-shore wave transformation, from an offshore global swell model such as NOAA WaveWatch3, is an economical means to arrive at swell size estimates at particular locations of interest. Recently, some work (e.g. Browne et al. (2006)) has examined an artificial neural network (ANN) based, empirical approach to wave estimation. Here, we provide a comprehensive evaluation of two data driven approaches to estimating waves nearshore (linear and ANN), and also contrast these with a more traditional spectral wave simulation model (SWAN). Performance was assessed on data gathered from a total of 17 near-shore locations, with heterogenous geography and bathymetry, around the continent of Australia over a 7 month period. It was found that the ANNs out-performed SWAN and the non-linear architecture consistently out-performed the linear method. Variability in performance and differential performance with regard to geographical location could largely be explained in terms of the underlying complexity of the local wave transformation.

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Section: Introductionmentioning

confidence: 99%

Near-shore swell estimation from a global wind-wave model: Spectral process, linear, and artificial neural network models

Browne

Castelle

Strauss

et al. 2007

Coastal Engineering

103

View full text Add to dashboard Cite

show abstract

“…In recent years, ANNs have found an increasing application to oceanic and atmospheric sciences and engineering (Hsieh and Tang 1998). Some examples include investigations pertaining to the estimation of salinity and density (Krasnopolsky et al 2002), phytoplankton production (Scardi and Harding 1999), temperature profiles (Churnside et al 1994), ocean color (Gross et al 1999), precipitation (Hong et al 2004Liu et al 2001;Marzban and Witt 2001), wind speeds (Kretzschmar et al 2004;Lee and Jeng 2002;More and Deo 2003), tidal water levels (Lee et al 2002;Cox et al 2002), radiative flux (Loukachine and Loeb 2003), wind and wave loads on structures (Haddara and Soares 1999;Mase and Kitano 1999), barge motions (Mahfouz and Haddara 2003), scour depths near pilings (Kambekar and Deo 2003), etc.…”

Section: Introductionmentioning

confidence: 99%

One-Day Wave Forecasts Based on Artificial Neural Networks

Londhe

Panchang

2006

Journal of Atmospheric and Oceanic Technology

110

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Sophisticated wave models like the Wave Model (WAM) and Simulating Waves Nearshore (SWAN)/WAVEWATCH are used nowadays along with atmospheric models to produce forecasts of ocean wave conditions. These models are generally run operationally on large ocean-scale domains. In many coastal areas, on the other hand, operational forecasting is not performed for a variety of reasons, yet the need for wave forecasts remains. To address such cases, the production of forecasts through the use of artificial neural networks and buoy measurements is explored. A modeling strategy that predicts wave heights up to 24 h on the basis of judiciously selected measurements over the previous 7 days was examined. A detailed investigation of this strategy using data from six National Data Buoy Center (NDBC) buoys with diverse geographical and statistical properties demonstrates that 6-h forecasts can be obtained with a high level of fidelity, and forecasts up to 12 h showed a correlation of 67% or better relative to a full year of data. One limitation observed was the inability of the artificial neural network model to correctly predict the magnitude of the highest waves; although the occurrence of high waves was predicted, the peaks were underestimated. The inclusion of several years of data and the judicious selection of the training set, especially the inclusion of extreme events, were shown to be crucial for the model to recognize interannual variability and provide more reliable forecasts. Real-time simulations performed for April 2005 demonstrate the efficiency of this technology for operational forecasting.

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“…In order to simulate the measured displacement responses, noise-contaminated inputs are used. The two types of displacement responses on the "ve points in the time history are used as inputs, which are calculated using HNM, and all the values of inputs and outputs in the training samples are normalized according to equation (15). For comparison and validation of the presented modi"ed BP algorithm, Figure 8 shows the convergence of the error norm for the NN model during the initial training stage.…”

Section: Resultsmentioning

confidence: 99%

“…Examples include the reconstruction of constitutive properties using depth}load responses [12] and using either group velocities, phase velocities or slowness measurements [13], and estimation of contact forces from impact-induced strain [14] and prediction of impact contact forces [15]. There is generally a well-de"ned forward problem that may have a solution, but the inverse problem is often ill-posed, and conventional approaches often require computationally intensive iterative processes to "nd a solution.…”

Section: Introductionmentioning

confidence: 99%