# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 1 Introduction
A more detailed comparison of physical models (PMs) and data-driven models (DDMs) is provided by the comprehensive review of related works in [13], where it is mentioned that DDMs provide more accurate near- and real-time predictions but require computationally expensive training and can occasionally yield physically implausible results due to their statistical nature. The current study exploits data-driven models based on machine learning algorithms for the development of a hull and propeller biofouling assessment framework, which could support optimization of the decision-making process for maintenance actions. From a practical viewpoint, the usage of DDMs for the assessment of the fouling status can present several challenges. Firstly, the available dataset can contain several years of operation, including maintenance actions, that affect the ship's performance. The methods and the respective models needed for the assessment of the fouling status require the proper selection of datasets to be used for training. This aspect is not significantly highlighted in previous works and, in the current study, we aim to address several issues related to the choice for model development and dataset selection when defining baseline performance or evaluating performance over time. Specifically, we analyze two methods, each offering a unique approach to address the performance assessment problem. Furthermore, the aspects of data preparation and feature selection, based on the available parameters, also require in-depth analysis. Therefore, the objectives of the study are summarized as follows:  
* To prepare a dataset by properly identifying the time periods that would entail meaningful information for the fouling problem by employing a suitable KPI. * To appropriately compile multiple data sources to enhance, complete, and address any missing data. * To apply suitable data pre-processing techniques to derive a quality dataset that will focus on the physics of the problem. * To develop ML models aimed at capturing the impact of fouling development over time. * To identify the key features of the models by using informed statistical dependencies but also domain knowledge. * To evaluate various regression algorithms from the decision tree family and critically explore the hyperparameter space. * To use the models for a practical assessment that will assist in the optimization of maintenance. Therefore, the key contribution of the paper is the development of a methodology for the assessment of the biofouling impact on hull and propeller performance that systematically tackles the previously mentioned issues. Specifically, the paper aims to highlight the need to consider the physics of the problem for the informed development and application of ML models. The structure of the paper is as follows. In Section 2, a literature review of related works is provided, while Section 3 presents the ship used in the case study as well as the available sources of data acquisition utilized for the dataset compilation. Moreover, a comparison study of key statistics of the basic parameters of the problem is presented to examine the level of agreement and the activities needed for the completion of the database. Finally, the maintenance actions that occurred during the reporting period are defined, while an initial verification of these actions is performed using a KPI to reflect the performance changes at specific times. Section 4 describes the steps for developing the data-driven models and, specifically, the feature engineering related to the creation of new parameters and the identification of those more relevant to the problem. Then, the data preparation is presented concerning the application of a proper filtering and smoothing method. The central focus of this section is dedicated to showing the development of the model through the application of multiple ML algorithms and the fine-tuning of hyperparameters using quantified metrics. Ultimately, two distinct models are presented. Section 5 utilizes the models for the fouling assessment.