# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
###### Abstract  
As oil prices continue to rise internationally, shipping costs are also increasing rapidly. In order to reduce fuel costs, an economical shipping route must be determined by accurately predicting the estimated arrival time of ships. A common method in the evaluation of ship speed involves computing the total resistance of a ship using theoretical analysis; however, using theoretical equations cannot be applied for most ships under various operating conditions. In this study, a machine learning approach was proposed to predict ship speed over the ground using the automatic identification system (AIS) and noon-report maritime weather data. To train and validate the developed model, the AIS and marine weather data of the seventy-six vessels for a period one year were used. The model accuracy result shows that the proposed data-driven model has a satisfactory capability to predict the ship speed based on the chosen features.  
Keywords:ship speed over the ground; machine learning; ship fuel consumption; decision tree regression; ensemble methods +
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However, due to the difficulty in modelling actual sea surfaces and estimating the total energy system of a ship, inaccuracies are anticipated in the calculated results. To overcome this problem, a data-driven model has been proposed. [PERSON] and [PERSON] [6] investigated ship performance in terms of ship speed and engine power using the Gaussian process and polynomial regression models for single container ship data. [PERSON] et al. [7] proposed an algorithm to build an improved multilayer perceptron network for predicting long-term ship speed, by applying particle swarm optimization to optimize the hidden neurons of the multilayer perceptron. [PERSON] et al. [8] conducted a comparison study on a recurrent neural network, back propagation neural network, and support vector regression model to investigate the trajectory of a ship using a single ship in a certain area using automatic identification system (AIS) sensor data.  
Choosing the right machine learning model to evaluate ship speed and assess ship performance while sailing is always challenging, especially when applied to big data [9; 10; 11]. Applying a simple model, such as a linear regression, may not be precise enough [12]. In addition, it might be difficult to determine the features necessary to train the model and to tune the hyperparameters [13; 14]. This study proposes a maritime data analysis framework based on AIS and marine weather data to predict ship speed over the ground (SOG), which determines the most economical shipping route that can reduce fuel expenses. This framework includes data acquisition, preprocessing such as denoising, feature extraction, and model generation. To generate the model for SOG, various machine learning regression techniques are employed, such as, linear regression (LR), polynomial regression, decision tree regressors (DTRs), gradient boosting regressors (GBRs), extreme gradient boosting regressors (XGBRs), random forest regressors (RFRs), and extra trees regressors (ETRs) where their parameters are optimized through hyperparameter tuning. Using real ship route data, the computational time and accuracy of each method were compared through model validation, and the most accurate and efficient method was validated for various ship routes and ship types. The developed methodology in this study is expected to be used to train the best models for the SOG prediction of ships, which aims to track the performance of ships, and finally be used for actual ship route optimization purposes.  
The remaining sections of this study are organized as follows. Section 2 describes the suggested methodology which includes data pre-processing, formulation of the regression models, parameter tuning methods, and model verifications. Section 3 explains the details of the case study and offers a discussion of the results. Section 4 provides the overall conclusions of the study.