# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 7 Conclusions  
In conclusion, a hull and propeller condition monitoring tool for a bulk carrier ship using operational data collected over a period of two-and-a-half years is presented. The analysis utilized high-frequency data and noon reports, while a preliminary assessment of the vessel's hydrodynamic conditions was conducted using a suitable KPI, which allowed for the identification of performance degradation due to fouling and the impact of maintenance events. Furthermore, the study implemented a data-driven approach to predict the SHP in realistic operational conditions. Following feature engineering and data preparation,various machine learning algorithms from the decision tree family were evaluated, and an extensive exploration of hyperparameters was performed to identify the model with the best generalization capability. As a result, a MAPE of 1.1% was achieved on previously unseen test data. A model trained on data collected shortly after maintenance was applied to data collected at a later stage after maintenance. The PI, defined as the normalized difference between the actual and predicted SHP, was utilized to describe the deterioration of the vessel's condition. When applied to a dataset reflecting advanced stages of biofouling accumulation, the model predicted that if the vessel were in a clean state and operated under identical conditions, there would be an estimated average increase of 11.3% in power requirements. In a separate approach, the Days feature was incorporated to indicate the elapsed time since the previous cleaning event, thus enabling emulation of scenarios that referred to different points in time. A model incorporating this feature was trained using a dataset spanning over a year, and it was then applied to synthetic datasets representing both laden and ballast conditions across a range of vessel speeds, with various values of the Days feature. By comparing the predictions made for different Days feature values, while keeping the other features constant, the PI resulting from biofouling was derived. By comparing the model predictions on synthetic data 300 days apart, while keeping other parameters constant, it is determined that the fouled vessel required an average 20.5% increase in power compared to a clean state. These data-driven approaches provide an estimation of the PI caused by marine biofouling growth within a specific timeframe. By incorporating SFOC and fuel cost information, the subsequent daily increase in fuel cost could be determined. As a result, it was estimated that over a span of approximately 10 months, biofouling led to an average daily fuel cost increase of $2228. Shipping companies can compare the cumulative additional fuel cost with the cost of maintenance, considering the vessel schedule, to strategically determine the optimal timing to perform vessel cleaning. Hence, by providing valuable insights and practical implications, the findings of this study enable cost-effective decision making within the shipping industry. These insights support the development of more informed and efficient maintenance strategies, ultimately leading to improved operational efficiency. Conceptualization, N.T. ; Methodology, N.T. and G.N. ; Validation, G.N. ; Formal analysis, N.T. and G.N. ; Investigation, G.N. ; Resources, N.T. and V.Z. ; Data curation, G.N. ; Writing--original draft, N.T. and G.N. ; Writing--review & editing, N.T., G.N. and V.Z. ; Visualization, G.N. ; Supervision, N.T. and V.Z. ; Project administration, N.T. and V.Z. ; Funding acquisition, N.T. and V.Z. All authors have read and agreed to the published version of the manuscript. The present work was supported by the Retrofit55 project (Retrofit solutions to achieve 55% GHG reduction by 2030), which has received funding from the European Union's Horizon Europe Research and Innovation Program under Grant Agreement No. 101096068. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.