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In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.

Predictive analytics utilizing machine learning algorithms play a pivotal role in various domains, including the profiling of carbon dioxide (CO2) emissions. This research paper delves into an extensive exploration of different algorithms, encompassing neural networks with diverse architectures, optimization, training, ensemble, and specialized algorithms. The primary objective of this research is to evaluate the efficacy of supervised and unsupervised algorithms, including Deep Belief Networks, Feed Forward Neural Networks, Gradient Boosting, and Regression, as well as Convolutional Neural Networks, Gaussian, Grey, and Markov models, and clustering and optimization algorithms. The study places particular emphasis on data-driven methodologies and cross-validation techniques with an evaluation of the learning models entailing comprehensive training, validation, and testing, employing evaluation metrics such as R2, MAE, and RMSE. The study employs correlation analysis to examine the relationship between input parameters and emission characteristics. The research highlights the advantageous attributes of these algorithms in accurately forecasting CO2 emissions, evaluating energy sources, improving prediction accuracy, and estimating emissions. Notably, deep learning, Artificial Neural Networks (ANN), and Support Vector Machines (SVM) demonstrate effectiveness across diverse industries, while the Modified Regularized Fast Orthogonal-Extreme Learning Machine (MRFO-ELM) algorithm optimizes predictions specifically related to coal chemical emissions. Hybrid techniques demonstrate accuracy in predicting carbon emissions and energy consumption, whereas gray prediction models provide reliable estimates even with limited data. However, it is important to acknowledge certain limitations, including data requirements, potential inaccuracies arising from complex factors, constraints faced by developing countries, and the impact of electric vehicle expansion on the power grid. To optimize models, a survey is conducted, involving customization of parameters and learning rates, while exploring various performance metrics to evaluate model accuracy. The research outcomes contribute to the effective monitoring of CO2 emissions in operational environments, thereby aiding executive decision-making processes. [ABSTRACT FROM AUTHOR]