Wave tranquility studies using neural networks

Information on heights of waves and their distribution around harbor entrances is traditionally obtained from the knowledge of incident wave, seabed and harbor characteristics by using experimental as well as numerical models. This paper presents an alternative to these techniques based on the computational tool of neural networks. Modular networks were developed in order to estimate wave heights in and around a dredged approach channel leading to harbor entrance. The data involved pertained to two harbor locations in India. The training of networks was done using a numerical model, which solved the mild slope equation. Test of the network with several alternative error criteria confirmed capability of the neural network approach to perform the wave tranquility studies. A variety of learning schemes and search routines were employed so as to select the best possible training to the network. Mutual comparison between these showed that the scaled conjugate method was the fastest among all whereas the one step secant scheme was the most memory efficient. The Brent's search and the golden section search routines forming part of the conjugate gradient Fletcher–Reeves update approach of training took the least amount of time to train the network per epoch. Calibration of the neural network with both mean square as well as the sum squared error as performance functions yielded satisfactory results.© Elsevie

Artificial neural networks for wave propagation

Wave propagation around and inside a harbor is conventionally studied by numerically solving a representative equation of short-wave progression or by taking actual measurements on a physical model. Although the numerical schemes yield workable solutions, underlying assumptions as well as noticeable difference between the resulting estimations and actual measurements leave scope to employ alternative approaches. The current study is an attempt in that direction and is based on the approach of neural networks. Modeled to imitate the biological neural network prevalent in human brains, an artificial neural network represents interconnection of computational elements called neurons or nodes, each of which basically carries out the task of combining inputs, determining their strength by comparing the combination with a bias (or alternatively passing it through a non-linear function) and firing out the result in proportion to such a strength. The network is first trained with examples, the strengths of interconnections (or weights) are accordingly fixed and then it is readied for application to unseen inputs. The applications of neural networks have now spread across all disciplines of ocean engineering, namely, harbor, coastal, offshore and deep-ocean engineering, and are directed towards function approximation, optimization, system modeling including parameter predictions. Advantages of the ANN schemes are improved accuracy, ease in application, reduced data requirement and so on. In the present work a feed forward modular neural network was developed in order to estimate attenuation of wave heights along the approach channel of a harbor starting from seaward boundary and ending at the harbor entrance. The trained network was found to satisfactorily follow the expected trend of wave height attenuation along the harbor channel. When tested for unseen input it yielded values of wave heights close to the numerical and physical models. The network also properly simulated the effect of variation of wave period as well as that of angle of wave attack on wave attenuation

Inverse modeling to derive wind parameters from wave measurements

The problem of deriving wind parameters from measured waves is discussed in this paper. Such a need reportedly arises in the field when the wind sensor attached to a wave rider buoy at high elevation from the sea level gets disconnected during rough weather, or otherwise needs repairs. This task is viewed as an inverse modeling approach as against the direct and common one of evaluating the wind-wave relationship. Two purely nonlinear approaches of soft computing, namely genetic programming (GP) and artificial neural network (ANN) have been used. The study is oriented towards measurements made at five different offshore locations in the Arabian Sea and around the western Indian coastline. It is found that although the results of both soft approaches rival each other, GP has a tendency to produce more accurate results than the adopted ANN. It was also noticed that the equation-based GP model could be equally useful as the one based on computer programs, and hence for the sake of simplicity in implementation, the former can be adopted. In case the entire wave rider buoy does not function for some period, a common regional GP model prescribed in this work can still produce the desired wind parameters with the help of wave observations available from anywhere in the region. A graphical user interface is developed that puts the derived models to their actual use in the field.© Elsevie

Genetic programming for real-time prediction of offshore wind

Wind speed and its direction at two offshore locations along the west coast of India are predicted over future time-steps of 3 to 24 hrs based on a sequence past wind measurements made by floating buoys. This is done based on a relatively new soft computing tool using genetic programming. The attention of investigators has recently been drawn to the application of this approach that differs from the well-known technique of genetic algorithms in basic coding and application of genetic operators. Unlike most of the past works dealing with causative modeling or spatial correlations, this study explores the usefulness of genetic programming to carry out temporal regression. It is found that the resulting predictions of wind movements rival those made by an equivalent and more traditional artificial neural network and sometimes appear more attractive when multiple-error criteria were applied. The success of genetic programming as a modelling tool reported in this study may inspire similar applications in future in the problem domain of offshore engineering, and more research in the computing domain as well

Real time wave and wind forecasting system for the indian coastline

Real time forecasts of wind and waves are useful in taking many operation oriented decisions in the ocean. When site specific forecasts are required time series modeling can be viewed as advantageous over elaborate numerical methods. This paper discusses time series models based on the soft computing approaches of genetic programming (GP) artificial neural network (ANN) to obtain real time forecasts of wind speed, its direction as well as the significant wave height at different locations along the Indian coastline where continuous wave buoy observations get collected. All the developed models over various locations have been integrated into a graphical user interface (GUI) to facilitate web based real time implementation of these models. The GUI starts with a figure showing locations of buoys. The user has to click on the concerned location where it is desired to have the forecasts for next 24 hours. The appropriate and calibrated ANN and GP models will then come into background, linking them with the most recently observed data and will accordingly yield the forecasts immediately

A Coupled Numerical and Artificial Neural Network Model for Improving Location Specific Wave Forecast

As more than a quarter of India’s population resides along the coastlines, it is of utmost importance to predict the significant wave height as accurately as possible to cater the needs of safe and secure life. Presently Indian National Centre for Ocean Information Services (INCOIS) provides wave height forecasts on regional as well as local level ranging from 3 hours to 7 days ahead using numerical models. It is evident from numerical model forecasts at specific locations that the significant wave heights are not predicted very accurately. The obvious reason behind this is the ‘wind’ used in these models as a forcing function is itself forecasted wind (ECMWF wind (European Centre for Medium-range Weather Forecasting)) and hence many times the forecasts, differ very largely from the actual observations. These models work on larger grid size making it as major impediment in employing them particularly for location specific forecasts even though they work reasonably well for regional level. Present work aims in reducing the error in numerical wave forecast made by INCOIS at four stations along Indian coastline. For this ‘error’ between forecasted and observed wave height at current and previous time steps was used as input to predict the error 24 hr ahead in advance using ANN since it has been effectively used for wave forecasting (univariate time series forecasting in general) since last two decades or so. This predicted error was then added or subtracted from numerical wave forecast to improve the prediction accuracy. It is observed that numerical model forecast improved considerably when the predicted error was added or subtracted from it. This hybrid approach will add to the usefulness of the wave forecasts given by INCOIS to its stake holders. The performance of improved wave heights is judged by correlation coefficient and other error measures like RMSE, MAE and CE