# A Machine Learning Predictive Model for Ship Fuel Consumption
## 1 Introduction  
Shipping is crucial for global economic development, with 90% of goods transported between ports [1, 2]. Maritime trade is expected to grow from 10 billion tons in 2014 to 20 billion tons by 2030 [3]. In the Amazon, waterway transport of passengers and cargo is vital, with 10 million passengers and 5 million tons of cargo moved in 2017 [4]. The North region of Brazil, with its extensive waterway network, depends on these routes for commodity export to European, Asian, and North American markets [5]. For economically sustainable navigation in the Amazon, the knowledge of the buoyancy of water levels [6] and the hydrodynamic and energetic characteristics of rivers [7] presents great importance in estimating the fuel consumption of Amazonian vessels. The aging Brazilian fleet, with vessels often over 20 years old, impacts fuel consumption [8].  
Shipping companies seek efficient, sustainable energy use to reduce costs and meet environmental standards such as IMO and SOLAS [9]. Fuel consumption accounts for two-thirds of voyage costs and over 25% of total operating costs [10]. In 2018, 900 million tons of CO\({}_{2}\) were emitted, a figure expected to rise [11]. Efficient fuel management is critical and is influenced by factors such as speed, draft, displacement, weather, hull, and propeller roughness [12, 13]. Studies have explored various methods to reduce fuel consumption and minimize environmental impacts. These methods include hull cleaning [14], trim adjustment [15], and the use of alternative energies like wind [16], solar [17, 18], and wave energy [19], as well as fuel cells [20]. The studies also cover how speed reduction impactsfuel consumption [21; 22]. However, there is limited research on fuel consumption in the Amazon region.  
Characterizing relationships between parameters and fuel consumption is complex, making it challenging for companies to prioritize parameters for fuel reduction. Developing a model to reveal key factors affecting fuel consumption can optimize efficiency [23]. A robust modeling framework is needed to consider relevant parameters and their interdependencies [24]. Machine learning methods offer better results compared to traditional approaches in predictive and prescriptive analysis [23].  
This research develops a predictive model for fuel consumption using vessel data from the Amazon region. It includes data collection, feature selection, and model validation. Techniques such as Decision Tree, Random Forest, Extra Tree, Gradient Boosting, Extreme Gradient Boosting, and CatBoost were used [25; 26; 27]. Performance was assessed using metrics like computational time. The most accurate model was validated across different vessel types and Amazon regions [28; 29].  
[PERSON] and [PERSON] [30] compared Random Forest, Neural Networks, and Gradient Boosting for public bus fuel prediction in Sri Lanka, finding Random Forest methods to be the most accurate. [PERSON] et al. [31] developed an ANN model for vessel fuel consumption, evaluated by RMSE and R\({}^{2}\). [PERSON] et al. [32] compared regression algorithms, finding Extra Tree the best. [PERSON] et al. [33] used various models, with Multiple Linear Regression providing the closest estimates. [PERSON] et al. [34] applied CatBoost for port stay estimation using historical data, evaluating performance with MAE and MSE. [PERSON] et al. [25] predicted cruising speed using multiple models, with Extra Tree performing best. [PERSON] et al. [26] integrated Random Forest, XGBoost, and Multiple Linear Regression (MLR) for fuel consumption prediction, while [PERSON] et al. [35] used Linear Regression, Polynomial Regression, and XGBoost for engine power prediction.  
The methodology in this research is applicable to fuel consumption models for vessels in the Amazon or similar regions.