# A Machine Learning Predictive Model for Ship Fuel Consumption
## 3 Analysis of Results
The other dimensional and physical attributes, such as breadth (\(+0.56\)), draft (\(+0.55\)) and depth (\(+0.42\)), presented the same behavior, whose  
Figure 5: Pearson’s Correlation matrix between the model features. relation is directly proportional in function of the fuel consumption, but with less correlative intensity when compared to the length. For the designer, it is extremely important to know the physical dimension of construction that will contribute to high fuel consumption since, opting for cost optimization, the length should be the first parameter to be reviewed. Since the drag coefficient and frictional resistance are functions dependent on the size of the ship, the longer the ship, the larger the contact area with respect to the fluid; this is an intuitive finding and is proven by the intrinsic correlation of the ships operating in the Amazon region. The inversely proportional correlation between wooden boats (\(-0.17\)) and fuel consumption becomes evident in (Figure 4). In a scenario where two boats have similar dimensions and identical operational conditions, but one is built in marine steel and the other in wood, the former will present a higher fuel consumption than the latter. In other words, boats made of marine steel (+0.42) tend to have a higher fuel consumption volume when compared to boats made of aluminum (+0.19), fiber (\(-0.074\)), and wood (\(-0.17\)). In contrast, from an economic point of view, boats designed with wooden hulls were more advantageous because of their lower fuel consumption. Hulls made of marine steel showed the most significant correlation with fuel consumption, about +0.42. The more pronounced roughness of the hull directly impacts the coefficient of friction. In addition, most of these vessels carry a high volume of cargo, requiring materials with greater strength and durability. Consequently, larger and larger combustion engines are required to sustain the propulsive power necessary to overcome the weight and the difficulty of displacement of the vessel. From the perspective of propulsive parameters, the number of generators showed the highest correlation (+0.39), followed by engine power (+0.31) and the number of thrusters (+0.28). It is understood that larger vessels adopt a redundant number of generators for safety factors: in the shutdown or malfunction of one generator, a second one should immediately be put on standby. Since the auxiliary engines (generators) are responsible for providing electrical power for the operation of the command and navigation equipment, the boat would be adrift or extremely susceptible to a collision with another ship in the event of a blackout. The engine power determines the carrying capacity and/or speed developed by the vessel. The greater the weight and dimensions or speed a vessel performs, the greater the power required to move it. Considering the performance curve of the vessel, fuel consumption will tend to increase as well. The number of thrusters, in turn, showed similar behavior: to achieve the necessary force for displacement or glide of the boat and to preserve the life of the engines, a greater number of thrusters is required. Ferry boat type vessels, intrinsic to the Amazon region on account of their large dimensions and cargo capacity used, including for transporting cars along with passengers, showed the highest correlation, +0.19, as to the premise of vessel types, followed by passenger and general cargo transport vessels (+0.15), flat (\(-0.09\)), passenger transport (\(-0.16\)), and speedboat (\(-0.19\)). Comparing the two extremes, it is understood that the higher the cargo capacity required, for example, for a ferry boat, the higher the fuel consumption, while the higher the speed performed by a speedboat, the lower the fuel consumption since the duration of the trip will be reduced. It is possible to infer, therefore, that the length of the trip is the attribute that most influences fuel consumption, followed by the use of wood as a hull, and thirdly, the length of the vessel.