Application of decision tree techniques for the Prediction of Significant Wave Height

Significant wave height estimates are necessary for many applications in coastal and offshore engineering and therefore various prediction models have been proposed in the literature for this purpose. In this study, the performances of Decision trees classification for prediction wave parameters were investigated. The data set used in this study comprises of wave data and over water wind data gathered from deep water location in Lake Ontario. The data set was divided into two groups. The first one that comprises of 26 days wind and wave measurement was used as training and checking data to develop tree models. The second one that comprises of 14 days wind and wave measurement was used to verify the models. Training and testing data include wind speed, wind direction, fetch length and wind duration as input variables and significant wave heights as output variable. The wave heights for whole data set are grouped into wave height bins of 0.25 m. Then a class is assigned to each bin. For evaluation of the developed model the mean of each class is compared with the observed data.