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