# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 1 Introduction  
To mitigate the effects of global climate change, there are increasing pressures on the maritime industry to reduce the environmental footprint of shipping. As early as 2011, the International Maritime Organization (IMO) adopted mandatory measures with the aim of optimizing the energy efficiency of ships. Since then, the IMO has been taking further action. Specifically, the IMO has already set a strategy to reduce greenhouse gases related to ship operations based on short-, medium-, and long-term measures [1], while the most recent were set out in the July 2023 revised strategy [2], which targets a 20% reduction in ship emissions by 2030 and a 70% reduction by 2040 compared to 2008 emission levels with the ultimate goal of near-zero emissions by 2050. However, beyond the regulatory framework that steers shipping toward more sustainable solutions, a more general interest is observed from the various parties involved (e.g., ship owners, operators, and charteers) to optimize the operational performance of ships, reduce fuel consumption, and, ultimately, reduce emissions. As many studies show (e.g., [3]), the fuel cost constitutes by far the greatest part (up to 50-70%) of ships' operational cost and, therefore, the reduction of fuel consumption per voyage is a priority for all shipping companies. For example, reducing it by just 1% can result in hundreds of thousands of dollars saved per year in the operation of large ships. An overview of the reduction potential achieved by the candidate measures is presented in [4, 5]. An operational measure that has been on the spot in the industry is the continuous monitoring of hull and propeller performance since it significantly affects fuel consumption used for propulsion. According to [6], fuel consumption is strongly related to the ship's drag force and, in particular, to the frictional resistance, which is affected by vessel hull biofouling. Biofouling is generated by the buildup of micro- and macro-organisms onto the ship hull. This results in an increase of the surface roughness and subsequently to an increase in the thickness of the boundary layer deteriorating the frictional resistance. In addition, an increase in the boundary layer thickness reduces the velocity of the water reaching the propeller, causing a reduction in the wake fraction. Finally, the development of biofouling on the propeller blades modifies their original profile and roughness, causing increases in the blade resistance and in the propeller torque, thus changing the propeller characteristics in free flow and resulting in an additional efficiency loss. Therefore, the development of biofouling on the hull and propeller has a significant effect on the overall hydrodynamic performance of a ship, with relevant research showing that thin biofilm and algae growth (slime, algae biofilm) cause a reduction in power efficiency of more than 10%, while the growth of calcareous/hard fouling (barnacles) can lead to an 80% increase in power request [7]. According to [8,9], the generation of biofouling on ship hulls is influenced by several factors, such as the operational conditions (speed and time moored or at anchor), the trading routes, and the seawater characteristics, as well as the maintenance actions concerning the anti-fouling coating as well as the vessel hull cleaning frequency. A standard approach for monitoring the effect of biofouling development on ship performance is achieved by calculating appropriate key performance indicators (KPIs). The KPIs act as a measure of the ship's actual performance using operational data against the ideal reference performance typically obtained from sea trials. This is the standard approach of ISO 19030 ([10]), which presents a default method using a physics-based approach to address the biofouling effect on hull and propeller performance. However, ISO 19030, when compared with more advanced models based on data-driven methods, is less accurate and provides results with higher uncertainty (see for example [11,12]).