A Machine Learning Model for Prediction of Marine Icing

Abstract

Abstract Marine icing due to freezing sea spray has been attributed to many safety incidences. Prediction of sea spray icing is necessary for operational safety, design optimization, and structural health. In general, lack of detailed full-scale measurements due to the complexity and costs make validation difficult. The next best option is that of controlled laboratory experiments. The current study is the first study in the field of sea spray icing that investigates the use of new data science technologies like machine learning and feature engineering for the prediction of sea spray icing based on data collected from controlled laboratory experiments. A new prediction model dubbed “Spice” is proposed. Spice is designed “bottom-up” from experimentally collected data, and thus, if the input variables are accurately known, it could be said to be highly accurate within the tested range compared to existing theoretical models. Results from the current study show promising trends; however, more experiments are suggested for increasing the range of confident predictions and reducing the skewness of the training data. Results from spice are compared with five existing models and give icing rates in various conditions in the middle of the spectrum of the other models. It is discussed how validation from two existing full-scale icing measurements from literature proves to be challenging, and more detailed measurements are suggested for the purpose of validation.