# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 2 Literature Review  
Focusing on the development of DDMs, several issues have been examined in previous works related to models aiming at the prediction of fuel consumption. The testing of the prediction accuracy of various machine learning and statistical methods using the same dataset is a typical research approach, as in the work of [14], where XGBoost, artificial neural networks (ANN), support vector regression, as well as statistical methods such as linear and polynomial regression and a generalized additive model are examined. The root mean square error and the complexity degree are used to compare the examined methods. Similarly, [15] examined different predictive models, such as multiple linear regression, ridge and LASSO regression, support vector regression, tree-based algorithms, and boosting algorithms, using data from noon reports and engine logbooks. For the validation of the predictive models, the K-fold cross-validation method was used, and a correlation analysis to identify the relationships among the different variables was carried out. A significant aspect is the selection of the features of the model. In [16], for the selection of the dependent variables to be used in model development, the \"domain knowledge\" and \"statistical method based on LASSO regularization\" methods were examined. The evaluation of these methods was based on the calculation of the mean absolute error, considering several combinations of variable feature selections and model training methods. In [17], the impact of different feature sets on the model's predictive accuracy was evaluated. The authors compared the error metrics obtained through cross-validation and testing of models trained with various combinations of features. On the other hand, in [18] a decision tree (DT) was used for the evaluation of feature importance and the selection of the key influencing factors of ship fuel consumption. The prediction model was developed by incorporating attention mechanisms into bidirectional long short-term memory (Bi-LSTM) networks. Bi-LSTM networks are a type of recurrent neural network (RNN) that can capture long-term dependencies in time series data. They consist of two LSTM layers that process the data in both forward and backward directions, allowing the network to have more context and a better understanding of the data stream. Attention mechanisms enable neural networks to focus on specific parts of the input data when making predictions. In addition, [19] examined several ML algorithms (multiple linear regression, LASSO, extra trees, XGBoost, LightGBT, and CatBoost) combined with a physics-based model (or else a white box model) for the prediction of ship fuel consumption. The latter is used as a new feature compared to the above-mentioned black box models and thus their combination is referred to as a gray box model. It was derived that the ensemble learning models examined, especially the gray box ones, present better prediction performance and generalization capabilities. In addition, the data-preprocessing phase is also a demanding task. For data cleaning, a suitable statistical procedure involving median filtering is implemented in [17], while in [20], measurements of each parameter that deviate more than three standard deviations from the mean are discarded. Another significant aspect is the generalization capability of DDMs compared with PMs. For example, in [21], an ANN was developed utilizing a dataset of two sea voyages, thus limiting the model applicability to the encountered operational conditions. Moreover, in [12], several ANN models based on different features for the assessment of hull and propeller degradation were developed. For feature selection, a random forest regressor was applied to evaluate the significance of each feature using \(p\)-value criterion. Data pre-processing was also carried out, including outlier removal, using a threshold deviation among two correlated parameters, while smoothing of the signals was performed using the simple moving average.