Machine learning tool for wave overtopping prediction based on the safety-operability ratio

Abstract

[Abstract:] The phenomenon of wave overtopping affects both the safety of people and port facilities, as well as the operability of a port. This work aims to develop a predictive tool based on artificial intelligence to predict wave overtopping over a breakwater, and to reach a compromise between safeguarding people and port efficiency. The data used came from an extensive field campaign lasting 6.5 years. During this research, the overtopping events were recorded and integrated with the forecast data available from the Port. The model used is the random forest, which was applied with the metrics AUC-PR and F2 score to a weighted dataset. The validation results show a recall for alignments of 0.91 and 0.89, while in the test it reaches 0.94 and 0.95. The dangerousness of the wave overtopping was also analyzed, reducing the dangerous false negatives to 2 and to 14 in the two alignments studied. Operability per alignment was unnecessarily affected during validation by 4 and 10 days over 26.5 months, and in the test by 4 and 3 days over 6.5 months. The results show a proven tool with real data, capable of predicting wave overtopping, and a methodology that can be exported to other ports.