Detection of plunging breaking waves based on machine learning
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Plunging breaking waves that occur in the vicinity of offshore structures can lead to high impulsive slamming loads, which are significant for the structural loading. The occurrence of plunging breaking waves is usually identified based on criteria that are derived from theoretical analyses and experimental studies. Given a large amount of data, detecting plunging breaking waves can be treated as a typical classification problem, which can be solved by a machine learning approach. In this study, logistic regression algorithm is used together with the experimental data from the WaveSlam project to train a classifier for the detection. Three normalized dimensionless features are introduced based on the measured data for the training. A classifier with respect to four wave parameters (i.e. water depth, wave height, crest height and wave period) is then explicitly developed for detecting plunging breaking waves. It is found that the trained classifier has an accuracy of 98.7% and F1 score of 99.2% for the tested data. Among the three dimensionless parameters, the ratio of wave height to water depth, H/d, is the most decisive factor for the detection of plunging breaking waves.