Improvement of Machine Learning-Based Modelling of Container Ship's Main Particulars with Synthetic Data

One of the main problems in the application of machine learning techniques is the need for large amounts of data necessary to obtain a well-generalizing model. This is exacerbated for studies in which it is not possible to access large amounts of data-for example, in the case of ship main data modelling, where a limited amount of real-world data (ship main data) is available for dataset creation. In this paper, a synthetic data generation technique has been applied to generate a large amount of synthetic data points regarding container ships' main particulars. Models are trained using a multilayer perceptron (MLP) regressor on both original and synthetic data mixed with original data points. Then, the authors validate the performance of the obtained models on the original data and conclude whether a synthetic-data-based approach can be used to develop models in instances where the amount of data on ship main particulars may be limited. The results demonstrate an improvement across almost all outputs, ranging between 0.01 and 0.21 when evaluated using the coefficient of determination (R2) and between 0.27% and 3.43% when models are evaluated with mean absolute percentage error (MAPE). This indicates that the application of synthetic data can indeed be used for the improvement of ML-based model performance. The presented study demonstrates that the application of ML-based syncretization techniques can provide significant improvements to the process of ML-based determination of a ship's main particulars at the early design stage. This paper suggests that, in cases where only a small dataset is available, artificial neural networks (ANN) can still be effectively employed to derive early-stage design values for the main particulars through the use of synthetic data.