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|Title: ||Wave simulation and forecasting using wind time history and data-driven methods|
|Authors: ||KAMBEKAR, AR|
|Keywords: ||artificial neural-networks|
m5 model trees
|Issue Date: ||2010|
|Publisher: ||TAYLOR & FRANCIS LTD|
|Citation: ||SHIPS AND OFFSHORE STRUCTURES, 5(3), 253-266|
|Abstract: ||Simulation and forecasting of significant wave heights and average zero-cross wave periods in real time are done for a specified location, given the past observed sequence of wind speed and wind direction. This is based on time series forecasting implemented using the two recent data-driven methods of genetic programming (GP) and model trees (MT). The wave buoy measurements made at eight different offshore locations around the west as well as the east coast in India are considered. Both genetic programming and model trees perform satisfactorily in the given task of wind-wave simulation and forecasting as reflected in the values of the six different error statistics employed to assess the performance of developed models over testing sets of data. Although the magnitudes of error statistics do not indicate a significant difference between the performance of GP and MT, qualitative scatter diagrams and time histories showed the tendency of MT to estimate higher waves more correctly.|
|Appears in Collections:||Article|
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