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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/12628

Title: Wave simulation and forecasting using wind time history and data-driven methods
Authors: KAMBEKAR, AR
DEO, MC
Keywords: artificial neural-networks
m5 model trees
prediction
parameters
coast
height
river
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.
URI: http://dx.doi.org/10.1080/17445300903439223
http://dspace.library.iitb.ac.in/xmlui/handle/10054/12628
http://hdl.handle.net/10054/12628
ISSN: 1744-5302
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